The impact of AMO (ability, motivation and opportunity ...
228
I The impact of AMO (ability, motivation and opportunity) model on Knowledge sharing in family controlled businesses in Hong Kong clothing industry. LEE, Yuk Ling Angie MBA & MGFM Submitted in fulfilment of requirements for Doctorate in Business Administration, The University of Newcastle, Australia September 2016
Transcript of The impact of AMO (ability, motivation and opportunity ...
I
The impact of AMO (ability, motivation and opportunity) model
on Knowledge sharing in family controlled businesses in Hong
Kong clothing industry.
LEE, Yuk Ling Angie
MBA & MGFM
Submitted in fulfilment of requirements for Doctorate in Business
Administration, The University of Newcastle, Australia
September 2016
II
Statement of Originality
The thesis contains no material which has been accepted for the award of any
other degree or diploma in any university or other tertiary institution and, to the best
of my knowledge and belief, contains no material previously published or written by
another person, except where due reference has been made in the text. I give consent
to the final version of my thesis being made available worldwide when deposited in the
University’s Digital Depository**, subject to the provisions of the Copyright Act 1968.
* * U n l e s s a n E m b a r g o h a s b e e n a p p r o v e d f o r a d e t e r m i n e d p e r i o d .
S i g n a t u r e … … … … … … … … … … … … . . D a t e … … … … … … … … .
III
Acknowledgements
I would like to offer my heartfelt gratitude to Dr.Ashish Malik for the guidance and patience
that he provided me to complete this dissertation.
I would also like to express my heartiest gratitude to all my lecturers and classmates
especially, Dr Kelvin Lo, Mr Man Lai Cheung, Alfred Cheng and Sindy Yau for their
knowledge and support throughout my studies. Special thank also go to Dr Philip
Rosenberger III for providing technical advice and guidance for my data analysis.
Thank you Associate Professor Guilherme Pires and Suzanne Ryan for guiding and
motivation. I would also like to thank my ex-lecturers Professor Andrew Sia and
Dr.Wing-Sun Liu for their support and invaluable suggestions.
Last but not least, my deepest appreciation to my family for their patience and support
throughtout the period.
IV
Content
STATEMENT OF ORIGINALITY ........................................................................................................ II
ACKNOWLEDGEMENTS ................................................................................................................... III
CONTENT ............................................................................................................................................... IV
ABBREVIATIONS ................................................................................................................................ XII
ABSTRACT .......................................................................................................................................... XIII
CHAPTER 1 .............................................................................................................................................. 1
INTRODUCTION ..................................................................................................................................... 1
1.1 INTRODUCTION ..................................................................................................................................................... 1
1.2 STUDY’S BACKGROUND ........................................................................................................................................ 5
1.3 RESEARCH OBJECTIVES ........................................................................................................................................ 5
1.4 RESEARCH PROBLEM AND QUESTIONS .............................................................................................................. 8
1.5 RESEARCH METHOD .......................................................................................................................................... 11
1.5.1 Data analysis ................................................................................................................................................... 11
1.5.2 Structure of the Thesis ................................................................................................................................ 12
1.5.3 Ethical considerations ................................................................................................................................. 12
1.6 EXPECTED CONTRIBUTIONS ............................................................................................................................. 13
1.7 CHAPTER SUMMARY .......................................................................................................................................... 14
CHAPTER 2 ........................................................................................................................................... 16
LITERATURE REVIEW ..................................................................................................................... 16
2.1 INTRODUCTION .................................................................................................................................................. 16
2. 2. KNOWLEDGE MANAGEMENT AND ITS CORE PROCESSES ........................................................................... 17
V
2.2.1 Knowledge sharing processes and its impact on firm performance ...................................... 20
2.3 RESEARCH ON FAMILY-CONTROLLED BUSINESSES ...................................................................................... 23
2.3.1 FCBs and knowledge sharing ................................................................................................................... 26
2.4 AMO: THE PERFORMANCE RUBRIC ............................................................................................................... 28
2.5 FCBS AND AMO MODEL ................................................................................................................................... 33
2.5.1 Ability and knowledge sharing ............................................................................................................... 33
2.5.2 Motivation and knowledge sharing ...................................................................................................... 35
2.5.3 Opportunity and knowledge sharing ................................................................................................... 38
2.6 FCBS AND AMO MODEL ................................................................................................................................... 40
2.6.1 FCBs and AMO ................................................................................................................................................. 41
2.7 RESEARCH GAP, KEY QUESTIONS AND HYPOTHESES DEVELOPMENT ........................................................ 42
2.7.1 Research Gap ................................................................................................................................................... 42
2.7.2 Research Questions ....................................................................................................................................... 44
2.8 CHAPTER SUMMARY .......................................................................................................................................... 46
CHAPTER 3 ........................................................................................................................................... 47
RESEARCH DESIGN AND METHODOLOGY ............................................................................... 47
3.1 INTRODUCTION .................................................................................................................................................. 47
3.2 RESEARCH PROCESS: PHILOSOPHY AND PARADIGMS .................................................................................. 48
3.2.1 Positivist research approaches ............................................................................................................... 49
3.2.2 Quantitative Research ................................................................................................................................. 50
3.2.3 Justification for a positivist and quantitative methodology ...................................................... 51
3.3 RESEARCH DESIGN ............................................................................................................................................. 52
3.4 RESEARCH QUESTION AND HYPOTHESIS DEVELOPMENT ............................................................................ 53
3.4.1 Research question ......................................................................................................................................... 53
3.4.2 Hypothesis development ............................................................................................................................ 55
3.5 CONCEPTUAL FRAMEWORK OF THE RESEARCH ............................................................................................ 56
3.5.1 Dependent variable ...................................................................................................................................... 57
3.5.2. Independent variables ................................................................................................................................ 60
3.5.3. Moderator ........................................................................................................................................................ 62
3.5.4 Additional background data .................................................................................................................... 62
VI
3.6 QUESTIONNAIRE DESIGN AND SAMPLING ...................................................................................................... 66
3.6.1. Measurement and scales ........................................................................................................................... 66
3.6.2 Data collection and sampling .................................................................................................................. 67
3.6.3. Defining the Research population ........................................................................................................ 68
3.6.4. Selection of sample ...................................................................................................................................... 69
3.6.5. Sampling frame .............................................................................................................................................. 69
3.6.6. Sample size ...................................................................................................................................................... 70
3.7. DATA COLLECTION METHOD ........................................................................................................................... 71
3.7.1. Administration of data collection ......................................................................................................... 71
3.7.2. Data analysis .................................................................................................................................................. 72
3.8. POWER OF TESTS OF INTERACTIONS ............................................................................................................ 72
3.9. DESCRIPTIVE STATISTICS ................................................................................................................................. 74
3.9.1. Reliability and validity ............................................................................................................................... 75
3.9.2. Reliability analysis with Cronbach’s alpha test .............................................................................. 77
3.9.3. Testing the moderating effect................................................................................................................. 77
3.10 SUMMARY AND LIMITATIONS ........................................................................................................................ 80
CHAPTER 4 ........................................................................................................................................... 82
DATA ANALYSIS AND RESULTS ................................................................................................... 82
4.1 INTRODUCTION .................................................................................................................................................. 82
4.1.1 Data preparation........................................................................................................................................... 83
4.1.2. Data coding and entry ............................................................................................................................... 84
4.2. SAMPLE PROFILE ............................................................................................................................................... 86
4.3 CHARACTERISTICS OF DEPENDENT AND INDEPENDENT VARIABLES ......................................................... 90
4.3.1 Profile of FCBs and Non-FCBs .................................................................................................................. 90
4.3.2 Descriptive statistics of items in this study ........................................................................................ 91
4.4 PRELIMINARY ANALYSIS ................................................................................................................................... 92
4.5 SKEWNESS AND KURTOSIS ............................................................................................................................... 93
4.6 TEST OF DISTRIBUTION NORMALITY .............................................................................................................. 94
4.7. SUMMARY OF DESCRIPTIVE DATA .................................................................................................................. 98
4.8 RELIABILITY AND VALIDITY OF MEASURED DATA ........................................................................................ 98
VII
4.8.1. Validity of measured data ........................................................................................................................ 98
4.8.2. Validity of independent and dependent variables ........................................................................ 99
4.9. RELIABILITY ANALYSIS .................................................................................................................................. 101
4.9.1. Ability (Training for Workers) ............................................................................................................ 101
4.9.2. Motivation (Incentive Systems) .......................................................................................................... 101
4.9.3. Opportunity (Trust) ................................................................................................................................. 102
4.9.4. Knowledge Sharing .................................................................................................................................. 102
4.9.5 Discriminant and Construct validity ................................................................................................. 103
4.10. HYPOTHESIS TESTING ................................................................................................................................. 104
4.10.1 Hypothesis 1.1 ........................................................................................................................................... 108
4.10.2 Hypothesis 1.2 ........................................................................................................................................... 108
4.10.3 Hypothesis 1.3 ........................................................................................................................................... 109
4.11. PROCESS MACRO IN SPSS FOR B ANALYSIS ............................................................................................. 110
4.11.1. Hypothesis 2.1 .......................................................................................................................................... 111
4.11.2. Hypothesis 2.2 .......................................................................................................................................... 113
4.11.3. Hypothesis 2.3 (H2.3) ............................................................................................................................ 117
4.12 SIMPLE SLOPE ANALYSIS .............................................................................................................................. 120
4.13 SUMMARY OF HYPOTHESIS TESTING .......................................................................................................... 121
4.14 CHAPTER SUMMARY ..................................................................................................................................... 122
CHAPTER 5 ......................................................................................................................................... 124
DISCUSSION AND CONCLUSION ................................................................................................ 124
5.1 INTRODUCTION ................................................................................................................................................ 124
5.2 MAJOR FINDINGS .............................................................................................................................................. 124
5.3 RESEARCH FRAMEWORK ................................................................................................................................ 125
5.4. DISCUSSION OF FINDINGS .............................................................................................................................. 126
5.5 MODERATING EFFECT OF FCBS .................................................................................................................... 129
5.6 THEORETICAL IMPLICATIONS ........................................................................................................................ 132
5.7 MANAGERIAL IMPLICATIONS ......................................................................................................................... 134
5.8 CONTRIBUTIONS .............................................................................................................................................. 136
5.9 LIMITATIONS AND FUTURE RESEARCH ......................................................................................................... 137
VIII
5.10 SUMMARY AND CONCLUDING REMARKS .................................................................................................... 140
Reference .......................................................................................................................143
APPENDICES ...................................................................................................................195
APPENDIX A EMAIL INVITATION .....................................................................................195
APPENDIX B: ORANGIZATION CONSENT FORM .............................................................197
APPENDIX C: ORGANIZATION INFORMATION STATEMENT ...........................................200
APPENDIX D: SURVEY ON BUSINESS PRACTICE ..............................................................204
APPENDIX E: Frequency Table ........................................................................................210 List of Tables
TABLE 3.4: QUESTIONNAIRE ROAD MAP……..……...…………………………………………………..…54
TABLE 3.5.1: MEASURING FORMAL AND INFORMAL KNOWLEDGE SHARING(DV)……….59
TABLE 3.5.2A: TRAINING FOR WORKERS …………………………………………………………………….61
TABLE 3.5.2B: INCENTIVE SYSTEMS ……………………………….……………………………………………61
TABLE 3.5.2C: TRUST…………………..……….……….……………………..……………………………………..61
TABLE 3.5.3: INDENTIFICATION OF FCBS……………………………………………………………………..62
TABLE 3.5.4: DEMOGRAPHIC QUESTIONS IN THE QUESTIONNAIRE ………………….…..…..65
TABLE 4.1.2 DATA CODING FOR ALL MEASURMENT VARIABLES………………………………….85
TABLE 4.2A: RESPONSE FREQUENCIES OF DEMOGRAPHIC DATA ……………………………..89
TABLE 4.3.1A: RESPONSE FREQUENCIES OF FCBS DATA …………..…………………..…………….90
TABLE 4.6A: TESTS OF NORMALITY ................................................................................. 94
TABLE 4.6B: DESCRIPTIVE ANALYSIS OF FCBS AND NON-FCBS IN THE HKCI .................... 97
TABLE 4.8.2: FACTOR ANALYSIS OF INDEPENDENT VARIABLES .....................................100
IX
TABLE 4.9.5: CORRELATIONS OF FACTORS IN THIS STUDY ..........................................104
TABLE 4.10A: MULTICOLLINEARITY TEST RESULTS IN MODEL 1 ....................................105
TABLE 4.10B: MULTICOLLINEARITY TEST RESULTS IN MODEL 2 ....................................106
TABLE 4.10.3: MODEL SUMMARY ..................................................................................110
TABLE 4.11.1A: MODEL SUMMARY IN BETWEEN TRAINING FOR WORKERS IN FCBS ...111
TABLE 4.11.1B: CONDITIONAL EFFECT OF TRAINING FOR WORKERS(X) AND
KNOWLEDGE SHARING(Y) AT VALUES OF FCBS (M) …..…………................................ 112
TABLE 4.11.1C: CONDITIONAL EFFECT OF TRAINING FOR WORKERS(X) AND
KNOWLEDGE SHARING(Y) AT VALUES OF FCBS (M) (JOHNSON-NEYMAN SIGNIFICANCE
REGIONS(S) ……………………………………………………………………………………........................... 113
TABLE 4.11.2A: MODEL SUMMARY IN BETWEEN INCENTIVE SYSTEMS IN FCBS ...........114
TABLE 4.11.2B: CONDITIONAL EFFECT OF INCENTIVE SYSTEMS(X) AND
KNOWLEDGE SHARING(Y) AT VALUES OF FCBS (M) …..………….................................. 115
TABLE 4.11.2C: CONDITIONAL EFFECT OF INCENTIVE SYSTEMS (X) AND
KNOWLEDGE SHARING(Y) AT VALUES OF FCBS (M) (JOHNSON-NEYMAN SIGNIFICANCE
REGIONS(S) ………………………………………………………………….…………………....................... 116
TABLE 4.11.3A: MODEL SUMMARY IN BETWEEN TRUST IN FCBS..................................117
TABLE 4.11.3B: CONDITIONAL EFFECT OF TRUST (X) AND KNOWLEDGE SHARING(Y) AT
VALUES OF FCBS (M) ………………………………………………....………….................................. 118
TABLE 4.11.3C: CONDITIONAL EFFECT OF TRUST(X) AND KNOWLEDGE SHARING(Y) AT
X
VALUES OF FCBS (M) (JOHNSON-NEYMAN SIGNIFICANCE REGIONS(S) …………………… 119
TABLE 4.13: SUMMARY OF HYPOTHESES TEST RESULTS. ...................................................122
List of Figures
FIGURE 1.4: FRAMEWORK AND RESEARCH QUESTIONS .................................................... 9
FIGURE 2.2: THE SECI MODEL (NONAKA & TAKEUCHI, 1995) .......................................... 19
FIGURE 3.1: OUTLINE OF CHAPTER 3 ............................................................................... 48
FIGURE 3.4.1: FRAMEWORK WITH RESEARCH QUESTIONS ............................................. 54
FIGURE 3.5: CONCEPTUAL FRAMEWORK ........................................................................ 57
FIGURE 3.6.2: THE STEPS OF THE RESEACH METHOD ...................................................... 67
FIGURE 3.9.3A: REGRESSION MODEL FOR ABILITY( TRAINING FOR WORKERS) AND FCBS
.......................................................................................................................................... 78
FIGURE 3.9.3B: REGRESSION MODEL FOR MOTIVATIONFRAMEWORK AND RESEARCH
QUESTIONS ....................................................................................................................... 79
FIGURE 3.9.3C: REGRESSION MODEL FOR OPPORTUNITY(TRUST) AND FCBS ................. 80
FIGURE 3.10: FLOW CHART OF THE MEASUREMENT METHODS USED IN THE RESEARCH
.......................................................................................................................................... 81
FIGURE 4.1: CONCEPTUAL MODEL FOR AMO MODEL IN KNOWLEDGE SHARING ........ 82
FIGURE 4.6: SUMMARY OF HISTOGRAMS FOR ALL VARIABLES IN THE MODEL .………… 95
FIGURE 4.10: OPERATIONAL MODEL KEY HYPTHOESIZED RELATIONSHIP BETWEEN AMO
XI
FACTORS AND KNOWLEDG SHARING… ..........................................................................107
FIGURE 4.10.1: OPERATIONAL MODEL FOR ABILITY (TRAINING FOR WORKERS) AND
KNOWLEDGE SHARING. ..................................................................................................108
FIGURE 4.10.2: OPERATIONAL MODEL FOR MOTIVATION (INCENTIVE SYSTEMS) AND
KNOWLEDGE SHARING.. .................................................................................................109
FIGURE 4.10.3: OPERATIONAL MODEL FOR OPPORTURNITY (TRUST) AND KNOWLEDGE
SHARING. ........................................................................................................................109
FIGURE 4.11.1: OPERATIONAL FOR ABILITY (TRAINING FOR WORKERS) AND
KNOWLEDGE SHARING ...................................................................................................111
FIGURE 4.11.2: OPERATIONAL FOR MOTIVATION (INCENTIVE SYSTEMS) AND
KNOWLEDGE SHARING ...................................................................................................112
FIGURE 4.11.3: CONCEPTUAL MODEL FOR OPPORTURNITY (TRUST) AND KNOWLEDGE
SHARING .........................................................................................................................113
FIGURE 4.12: SIMPLE SLOP RESULT FOR AMP FACTOR AND FCBS ................................120
FIGURE 5.3: AMO FACTORS APPLIED TO KNOWLEDGE SHARING AND ARE INDIVIDUALLY
MODERATED BY FCBS .....................................................................................................125
XII
Abbreviations
• FCBs- Family control businesses
• NonFCBs – Non Family control businesses
• HKCI – Hong Kong Clothing Industry
• AMO – Ability, Motivation, Opportunity
• TW – Training for workers
• IS – Incentive systems
• T – Trust
• KS – Knowledge sharing
• FK – Formal Knowledge
• IK – Informal Knowledge
• HKTDC – Hong Kong trading department council
XIII
Abstract ___________________________________________________________________________________________________ This study analyses the relationship between knowledge sharing, family controlled
businesses (FCBs), training for workers, incentive systems and trust in Hong Kong’s
Clothing Industry (HKCI). The study contributes by investigating the impact of the
ability, motivation and opportunity (AMO) paradigm focusing on training for
workers(A), incentive systems(M) and trust(O) and the moderating effects of Family
control businesses (FCBs) on knowledge sharing in Hong Kong’s clothing industry.
Such an investigation is timely and relevant when a number of Chinese family
businesses are facing the dilemma of succeeding their businesses through appropriate
governance structures, operations and systems so as to continue their entrepreneurial
spirit and effectively manage the generational transitions in Hong Kong (HK) (Au, K et
al. 2013).These challenges result in failure of some family control businesses from
managing succession and intergenerational leadership Issues (Chua et al., 2003; Long
& Chrisman, 2014). Thus, sharing key knowledge by people in FCBs through
appropriate people management practices is important for sustained succession in
FCBs.
The AMO paradigm has received considerable research attention in the field of Human
Resource Management (HRM) in the last two decades. The AMO model offers a useful
framework for studying how certain HRM practices can impact knowledge sharing
performance outcomes.
Based on a review of literature, a conceptual model showing the constructs of AMO was
developed and six hypotheses were then generated and tested in this research. The
findings of the research suggest that incentive systems and trust have a significant
impact on knowledge sharing but training for workers does not have any significant
XIV
impact on knowledge sharing. The findings also revealed that variables of training for
workers, incentive systems, and trust have a significant and negative impact for FCBs.
Overall, the findings from this study have implications for theory and practice. The
results highlight the relationships among the AMO components and Knowledge
sharing performance in a new context, especially by analysing the moderating impact
of FCBs. In terms of managerial implications for practice, this research highlights that
FCBs need to focus strategically on AMO components that contribute most in
enhancing a firm’s knowledge sharing performance.
1
Chapter 1
Introduction
1.1 Introduction
In Hong Kong, many Chinese family businesses are facing the dilemma of how to manage
and sustain their family business, often considering a range of options such as appropriate
governance structures, operations, and systems to sustain their entrepreneurial spirit and
to successfully pass on their businesses to future generations (Au et al. 2013). This study
attempts to address the above challenges by focussing on how family businesses can
avoid failure and improve succession of intergenerational leadership through the vital
processes of knowledge sharing (Chua et al., 2003; Long et al., 2014). The focus on
knowledge sharing in family businesses is relevant as knowledge has been regarded as a
critical resource for firms’ sustained performance and growth (Witherspoon, Bergner,
Cockrell & Stone, 2013). Earlier studies have argued that knowledge sharing is vital for
relaying critical business information from senior leadership to employees to achieve
sustained growth and profits (Kaplan and Norton, 2001, Quigley, 1994; Witherspoon et al.,
2013). While there have been several reviews of the literature on knowledge sharing and
its antecedents, these reviews often focus on an aspect of the wider knowledge
management literature or a specific industry sector (e.g. Grossman, 2007; Yahya & Goh
2002; van Rooi & Snyman, 2006). Witherspoon et al.’s (2013) recent meta-analytic review,
classified the literature into four key areas: intentions and attitudes of employees towards
knowledge sharing, organisational culture, rewards and gender as key foci of the studies
thus far. All three groups of antecedents: intentions and attitudes, organisational culture
2
and rewards tested positive towards knowledge sharing, however, there was no support
for the impact of gender; and country of origin was as a key moderator in knowledge
sharing behaviour. Their review of 46 studies points to several gaps in the research on this
important topic. There was only one study that focused on managers in Hong Kong (Chow
& Chan, 2008), using theory of reasoned action; not focused on family controlled
businesses; and finally, their review highlighted the need to understand the barriers to
knowledge sharing. With intentions, attitudes, culture and rewards being noted as
significant factors in explaining knowledge sharing behaviour, it is logical to pursue further
research that examines the role of human resource management (HRM) practices on
knowledge sharing. Further, given the limited focus on Hong Kong, this study argues that
subsequent generations of family businesses in the Hong Kong’s clothing industry (HKCI)
can benefit from understanding the key antecedents of knowledge sharing, especially in
the context of HKCI’s family-owned businesses.
The present research uses Hong Kong’s clothing industry (HKCI) as the main research
context, and focuses on the effect of family FCBs, training and skills, incentive systems,
and trust on knowledge sharing. Insights from this research are intended to contribute to
the literature on people management factors such as ability (training for workers,
motivation (incentive systems) and opportunity (trust) in the context of FCBs that are
central for knowledge sharing in HKCI.
This research is vital for addressing managerial problems that Chinese family businesses
are currently facing, especially with the concerns regarding the effectiveness of
governance structures, operations and systems to continue the entrepreneurial spirit in
the process of generational transition in HK (Au, K et al. 2013). These challenges are
worthy of attention as there is evidence of failure to retain family succession after a shift
in intergenerational leadership. (Chua et al., 2003; Long et al., 2014).
3
The AMO (ability, motivation, opportunity) model is a widely accepted model in Human
Resource management (HRM) literature and its linkages with firm performance. As noted
above, given the importance of cultural and human intentions and behavioural factors,
focusing on the AMO paradigm seems logical. Furthermore, this is also in line with earlier
research (Chua et al., 2004; Wong & Aspinwall, 2005; Mooradian, 2006; Zahra, 2007; Yang,
2007) of the three key AMO factors: ability (training for workers), motivation (incentive
systems) and opportunity (trust). This study will adapt constructs from this dominant AMO
paradigm and based on earlier studies (e.g., Salis & William, 2008; North, 2015) apply the
model in the context of HKCI’s FCBs on knowledge sharing. A particularly helpful aspect of
the AMO model is that it assumes that all AMO factors influence knowledge sharing and
we need to explore further of any moderating effect of FCBs (Boselie, 2012; Oudkerk Pool,
2016). The presence of relevant AMO variables in FCBs within the HKCI can help improve
our understanding of knowledge sharing and its consequent impact in enhancing the
competitive advantage and business performance of FCBs.
Limited studies have thus far investigated knowledge sharing among family-owned firms
in HK (Kontinen and Ojala, 2010; Lok and Crawford, 2004), especially in the clothing
industry (HKCI). The HK clothing industry is a reputable and an important manufacturing
sector in HK. It is the third largest manufacturing employer in HK, with around 900 firms,
employing around 5,773 people in the HKCI, and with revenues exceeding HK$ 143 billion
as of December 2015 (HKTDC, 30 June 2016). The HKCI imposes a powerful influence on
the global market, especially with its major exports to the United States and European
markets. It occupies a prominent position in HK’s domestic economy. The clothing
industry’s supply chain is well developed in HK ensuring the major clothing manufacturing
sector of HK with good quality standard, quality control and products supply, and logistical
arrangement, among others (Dickerson 1999).
4
Hong Kong is the only city in China that blends Chinese tradition with a British colonial
heritage influence (Enright et al., 1997; Henderson, 2001). Most firms may simply focus
on lower agency costs and shorter production lead times with little focus on the
importance of knowledge sharing to enhance firm performance (Lin, 2007). This study
investigates how family-owned firms share their knowledge with key stakeholders in their
business and the factors that have an impact on profitability.
This research fills the research gaps by evaluating the AMO independent variables
(training for workers, incentive systems and trust that is created by organizational
leadership) as well as exploring the moderating role of (FCBs) in knowledge sharing in HK.
Although a study examined the moderating role of technological capabilities in firms with
family ownership on knowledge sharing in the context of the United States (Zahra et al.,
2007), the findings cannot be easily applied to an Asian setting especially because HK is
well-known to exhibit an aspect of a crossvergent culture, a unique fusion of Western and
Eastern cultural context (Ralston, 2008., Ralston et al., 2008; Sarala and Vaara, 2010).
Furthermore, only few studies have focused on family controlled businesses (FCBs) in the
extant literature on knowledge sharing from a HR perspective (Chrisman et al.,2006; Miller
and Breton-Miller,2006). Thus this research is timely and will contribute to the emerging
body of literature on knowledge sharing from an Asian market context. Moreover,
despite an increasing number of research focusing on FCBs (Heck & Mishra, 2008), the
review literature reveals that there is a relatively limited body of research that focuses on
examining FCBs in relation to knowledge sharing.
5
1.2 Study’s background
Knowledge sharing has been regarded as a very important determinant of success since the
seminal work of Nonaka and Takeuchi (1995). Nonaka and Toyama (2003) as well as
Jasphapara (2004), using concepts from KM literature (such as knowledge creation, sharing,
and integration), highlighted the importance of knowledge sharing. Critical for the purposes
of this study is the influence of HRM practices using the AMO paradigm (such as training for
workers, incentive system, trust) on knowledge sharing.
Zahra (2007) noted there are challenges in efficiently measuring knowledge-sharing; in the
main it has two key elements: Formal and informal knowledge sharing that need to be
observed. The willingness and attitude of employees are among the prominent factors to
motivate effective knowledge sharing in firms. A firm’s performance and growth is often
affected by conflict between and unwillingness of family members and employees to share
information with others in the organization either because of the ownership issues or due
to some of family members not willingly wanting to make any changes (Hitt, et al., 2006;
Sirmon & Hitt, 2003; Zahra et al., 2006). This research will therefore examine the
moderating effect of FCBs on knowledge sharing in the context of HKCI using the established
human resource rubric of AMO.
1.3 Research Objectives
In view of the above, this research has the following overarching objectives:
1) To address the theoretical and empirical gaps examining the relationships between AMO
model, knowledge sharing and FCBs.
2) To explain the relationship AMO model has with knowledge sharing in the context of
HKCI.
6
3) To gain insights about the benefits of knowledge sharing for workers in the HKCI for
enhancing firm’s competitive advantage.
Addressing the first research objective, this study investigates the major antecedents of
knowledge sharing using the AMO model (Salis and William, 2008). Further, little research
has focused on knowledge sharing and examining the moderating effect of FCBs. Although
research interests on the topic of knowledge sharing is on the rise, there exists no
comprehensive understanding of how the AMO model variables (such as training for
workers, incentive systems, and trust) and FCBs impact on knowledge sharing. Pursuing
this research is vital as managing individual’s ability, motivation and opportunity to apply
their knowledge and skills has been considered as the dominant approach in HRM for
understanding how individual level performance can be enhanced. It can thus be argued
that if a person’s ability, motivation and opportunity needs are not addressed, their
performance (behaviour towards an organisational activity) such as in this case, knowledge
sharing, may be adversely affected.
Addressing the second research objective, the review of literature points that a vast
majority of research on knowledge sharing is based on research from Western nations or
other Asian economies, (see for example, Mooradian et al., 2006; Wong and Aspinwall,
2005; Witherspoon et al., 2013; Zahra et al., 2007). Thus, this study is timely as it will add
to the relatively limited research on knowledge sharing in Asia, especially in Hong Kong. The
complexity of Hong Kong’s post-colonial and Chinese cultural context not only represents a
challenge to researchers, but also offers an opportunity to improve both empirical
understanding and theoretical advancement of knowledge sharing.
The above importance of knowledge sharing has been well-established in the context of
Western nations since the early 1990s and has in the last decade attracted increased
attention in Asian countries (Davison & Ou, 2007). Firms now recognize that knowledge
7
management strengthens its competitive advantages and enhances its capability to
reduce agency costs, increase productivity, and shorter the lead times for production (Lin,
2007). However, there is still limited empirical basis for understanding the impact of
family members on knowledge sharing in firms (Lai, 2010). Barring a few studies that focus
on family controlled businesses (FCB) (Williamson, 1999; Heck & Mishra, 2008; Makadok,
2003), research gaps exist in relation to FCBs’ impact on knowledge sharing outcomes and
even more so, for FCB firms in the HKCI.
Hong Kong is developing into a business networking center for global clothing sources (Jin,
2004). Many clothing manufacturing firms in Hong Kong have already developed a strategic
mechanism for improving competitiveness and reducing resource disadvantages through
knowledge sharing using internal to external sources.
The research context of Hong Kong as the only Chinese city with a unique business culture
that combines elements of both Eastern and Western cultures, a strong educational
system and political organization offers this research the opportunity to explore the
problem in context (Enright, Scott & Dowell, 1997). Historically, firms in HK have been
doing business with Western firms directly and distinguish themselves from other Chinese
communities in Asia. However, studies of crossvergence suggest there are possibilities of
convergence and divergence occurring in HK’s business environment (Ralston et al., 2008).
Such changes provide a fertile ground for conducting intensive analysis about the
correlation between family FCBs, AMO factors (workers’ training, incentive systems and
trust) and knowledge sharing in the HKCI. Internal changes brought about by such
conditions may well alter the internal day-to-day transactions between employees, posing
new demand with varying degrees of hybrid cultures in their control systems.
Further, as HKCI comprises of majority of manufacturing firms and there is an insufficient
empirical research that has tested the association between knowledge sharing, AMO
8
variables and FCBs, thus this research is expected to improve our understanding of
knowledge sharing performance and competitiveness of HKCI.
Finally, in addressing the third research objective, this research aims to capture the
potential benefits for practitioners who are employed by the HKCI including clothing related
businesses such as materials suppliers, wholesalers, and retailers. It is anticipated that the
findings of this research will inform firms and knowledge workers about how to motivate
the workers to share knowledge. Through this research implication for decision makers and
practicing managers regarding how best to design and implement AMO factors to succeed
in knowledge sharing for enhancing firms’ competitiveness in the marketplace are likely to
be addressed.
1.4 Research problem and questions
It is argued that the management and leadership styles of owners in FCBs can potentially
affect their knowledge-sharing performance and motivate staff to share knowledge (Sull &
Wang, 2005). Further, most employees working in the HKCI undertake complex knowledge
work in design, logistics, manufacturing and operating highly automated machines for the
industry. In this context, the study’s research setting is suitable for exploring how
knowledge workers share their knowledge especially in FCBs. The literature on knowledge
workers suggests that “knowledge workers predominantly do work and solve challenges
that have already been done and solved before their organizations.” (Marketwired-
viewed on December 10, 2014). Drucker (1999) stated that knowledge worker
productivity is a crucial management resource for the 21st century and that knowledge is
regarded as a key element in a firm to improve its business performance (Arthur &
Huntley, 2005). Individual knowledge sharing contributes to sustainable competitive
9
advantages and a firm’s knowledge management practices (Apshvalka and Wendorff,
2005).
To fully investigate HKCI’s context, this research employs a quantitative survey of HKCI’s
businesses and uses factor analysis to test the AMO theoretical framework and its impact
on knowledge sharing behavior. It further explores whether FCBs acts as moderator. Based
on a review of the literature (covered in detail in the next chapter- Chapter 2), this study’s
research questions aim to address the identified gaps in the literature and forms the basis
of the study’s guiding theoretical framework (See Figure 1.4 below).
Figure 1.4 Framework and Research Questions
As this research investigates the relationship between antecedents of ability (training
workers), motivation (providing incentive systems), opportunity (creating an environment
of trust) of employees, ownership (FCBs) with knowledge sharing behaviors in the HKCI,
three research questions and six hypotheses have been formulated:
10
Research Questions:
Q1 Does ability (training workers), motivation (providing incentive systems), opportunity
(creating an environment of trust) of employees have a significant effect on knowledge
sharing in the HK clothing industry (HKCI)?
Q2 what are the key relationships between FCBs, AMO factors and knowledge sharing in
the HKCI firms?
Hypotheses
Hypothesis H1.1:
In the HKCI, training for workers is positively related to knowledge sharing.
Hypothesis: H1.2
In the HKCI, incentive systems are positively related to knowledge sharing.
Hypothesis H1.3
In the HKCI, trust is positively related to knowledge sharing.
Hypothesis H2.1
In the HKCI, FCBs act as a moderating factor in the relationship between training for workers
and knowledge sharing.
Hypothesis H 2.2
In the HKCI, FCBs act as a moderating factor in the relationship between the incentive
systems and knowledge sharing.
11
Hypothesis H2.3
In the HKCI, FCBs act as a moderating factor in the relationship between trust and knowledge
sharing.
1.5 Research Method
This section provides a brief overview of the research methodology employed. Details of
this will be further elaborated in Chapter 3 of the thesis.
1.5.1 Data analysis
This research involves a survey of 900 HK clothing industry firms through an internet-based
questionnaire from geographically dispersed firms in the HKCI. The target is to obtain 100
responses from senior executives such as top management, executives, and managers. A
quantitative survey is deemed as the most effective method for data collection, especially
when a large sample of quantitative data needs to be collected and analyzed (Saunders,
Lewis and Thornhill, 2011). This study follows the design employed in previous research
(Chua et al., 2004; Mooradian, 2006; Wong & Aspinwall, 2005; Zahra, 2007) and adapts the
questions employed by earlier studies to fulfil the research objectives of this study. The
reliability and validity of each theoretical construct will be verified using established tests.
A simple convenient sampling was adopted in the HKCI in which family-owned businesses
and non-family owned businesses have not been established conclusively. Next, the
sampling frame will comprise of a list of assigned directions and significant elements drawn
through a representative sample (Malhotra 2008).
12
Using a seven-point Likert scale, various theoretical constructs were measured through
established items from the literature and administered through an anonymous self-
completion online survey. The three AMO factors (training for workers, incentive systems,
and trust) and FCBs were analyzed. Several statistical tests were used to measure the
information collected in the online questionnaire and to test whether the research results
supported the hypotheses. Analytic techniques employed include descriptive analysis,
factor analysis, Pearson’s product moment correlation, and multiple regression analysis
using Process Macro in SPSS (Hayes, 2013), SPSS software (Version 22) for reliability, validity,
and testing the study’s hypotheses (Hair, 2006).
1.5.2 Structure of the Thesis
There are five chapters for this thesis. Chapter 1 sets the introduction and background, and
includes a briefing of the study’s research questions, hypothesis and the rationale for
conducting the study. Chapter 1 concludes with a short note on ethical considerations that
have been adhered to in this study and the expected contributions this study seeks to make.
Chapter 2 presents a comprehensive review of the extant literature on knowledge sharing
with the aim of clearly delineating the gap in the literature that this study aims to address.
Chapter 3 states the research methodology and design, and introduces the research
framework, whereas Chapter 4 provides the data analysis techniques and results. Chapter
5, the final chapter, discusses the results and concludes with implications for practice and
future research.
1.5.3 Ethical considerations
Confidentiality, anonymity, and consent were key ethical issues addressed in this research.
The student researcher complied with all the ethical standards set by the University of
13
Newcastle, Australia. This study treats all information collected from respondents with
confidentiality and has assured respondents through the use of an anonymous online
survey, wherein the respondents cannot be identified. The disclosure of individual
participants’ demographics and unique characteristics are also protected by the ethics
protocol (H-2015-0383) of this study.
Participation in the research is voluntary and as such organizations in the HKCI were emailed
with an invitation to participate in this study. By forwarding the invitational email and the
link to the study’s online questionnaire in the Participant Information Sheet document (See
Appendix for details), to senior executive who is in the position of a Manager/Top
executive/business ownership or owner of a family owned business in the HKCI, the
respective organisation may provide consent and then participate in this study.
Consentingparticipants were invited to complete an online questionnaire on knowledge
sharing in the HK clothing industry by clicking on the web link provided at the end of the
Participant Information Sheet in the invitational email. The information collected was
stored securely and remains strictly confidential. The data collected were kept in an
aggregate form and no individual or identifiable information would be released to others.
All materials collected will be available to the researcher for five years. No compensation
was given for participation. Ethical standards were thus observed as per the University’s
guidelines.
1.6 Expected Contributions
The findings from this study contribute towards a better understanding of the importance
of knowledge sharing in the context of FCBs in HKCI. The findings also form the basis for
related further studies in other business sectors as well as in other countries in relation to
14
the relationships covered in this study. This could also provide a wide range of valid data for
HKCI practitioners to develop knowledge sharing strategies.
In terms of policy contributions this study highlights the importance of training for achieving
better knowledge sharing outcomes, especially in FCBs and in developing organizational
policies to support this.
From a theoretical perspective, the use of the AMO model to explain how certain HRM
practices impact knowledge sharing is a key contribution to the literature on knowledge
sharing. Although some of the AMO factors are commonly used to explain the effect of
knowledge sharing, whether this model has been fully evaluated in the knowledge-sharing
field remains unclear. Hence, this study opens this future stream of analyzing a set of HRM
practices and its impact on knowledge sharing in a range of contexts
Finally, in terms of practical contributions for managers, this research explores the effect of
management, leadership, incentive systems, trust, and FCBs on knowledge sharing in HKCI.
Knowledge sharing needs to be understood in terms of both the formal and informal modes
in relation to the moderating effect of FCBs. Insights are derived for managers to
understand the critical success factors for future strategic planning purposes and allocating
resources that will enhance knowledge sharing behaviors and create opportunities for
sustained competitive advantages.
1.7 Chapter Summary
To summarize, this research aims to explore FCBs’ moderating effect on knowledge sharing
and aims to fill a gap in the literature on knowledge management and HRM by focusing on
15
knowledge sharing in the HKCI. Contributions at theoretical and managerial levels have
been identified. The changing business environment is forcing family owned clothing
industry firms to look for suitable strategies to improve their competitive advantages. By
considering the ownership type (FCBs) in the analysis, knowledge sharing understanding in
HKCI may help firms develop their capacity for business succession, especially in the
direction of intergenerational leadership.
16
Chapter 2
Literature Review
2.1 Introduction
The chapter develops the study’s research questions following a review of the literature.
Exploring how the relationship between knowledge sharing affects the performance in
family-controlled businesses (FCBs) by employing the commonly understood performance
rubric: the ability, motivation and opportunity (AMO) framework (Blumberg & Pringle,
1982; Vroom, 1964) is specifically reviewed. This is a widely used framework in the field of
HRM for analyzing performance drivers such as providing training for workers (ability),
offering adequate incentive systems (motivation), and creating a trusting environment
(opportunity) for employees to have a positive impact on knowledge sharing behaviors of
employees.
Knowledge has been regarded as a critical resource that can be shared through both
informal and formal ways (Nonaka, 1995). People communicate information, experiences,
insights, in different ways (Liao, 2007). While there is some recent interest that considers
the impact of HRM on a range of knowledge management processes (e.g. knowledge
sharing and knowledge transfer), there is little evidence of this gap being explored in the
context of FCBs in an Asian economy such as Hong Kong (Cabrera & Cabrera, 2005;
Minbaeva, Foss & Snell, 2009; Minbaeva et al., 2003; Minbaeva, 2013). To fill this
theoretical gap, the moderating effects of FCBs on knowledge sharing is included in the
research framework.
17
The rest of the chapter is structured as follows. The first part examines the theoretical
foundations of knowledge management (KM) and, more specifically, knowledge sharing.
The second part discusses the AMO model and its relationship with knowledge sharing.
The third part examines the process of influencing knowledge sharing and the intensity of
collaborative relationships between the AMO model and leadership patterns in FCBs. The
fourth part identifies the gaps in literature, leading to the development of the study’s
questions and hypotheses, which are tested with empirical data collected through
questionnaires.
2. 2. Knowledge Management and its core processes
Jasphapara (2004, p. 63) defines knowledge management (KM) as “effective learning
processed in relation to exploration, exploitation and sharing of individual knowledge
(tacit and explicit) that use appropriate technology and cultural environment to enhance
an organization’s intellectual capital and performance.” KM describes the processes and
strategies of collecting, transferring, utilizing, and protecting knowledge that can create
and provide sustainable competitive advantage (Kululanga and McCaffer, 2001; Lin,
2007b). KM practice includes identifying and managing new and existing knowledge to
develop new opportunities (Jarrar, 2002). The critical factors of this process are creating a
learning process, disseminating knowledge, and measuring knowledge capital in relation
to the total assets of an organization (Argot, 1999; Bontis et al., 2010; Sveiby and Risling,
1986).
Every individual’s knowledge sharing contributes to the success of a firm’s KM (Apshvalka
and Wendorff, 2005). Knowledge types can be broadly classified into two: tacit (i.e.,
informal) and explicit (i.e., formal). Tacit knowledge is highly personal and implanted in an
18
individual’s daily work practices (Nonaka, 1998, 2008), trust, and face-to-face interactions.
Informal structures also expedite tacit knowledge sharing between individuals (Koskinen
et al., 2003; Dholakia, 2002). By contrast, explicit knowledge is systematically and formally
stored in databases or libraries (Polanyi, 1966 cited in Nonaka, 1994) and manuals or
computer files (Aman, 2010; Ismail and Ashmiza, 2012). Tacit knowledge is difficult to
transmit because of its inherently instinctive and subjective nature (Richey and Klein,
2010).
Haldin-Herrgard (2000) point out that the diffusion of tacit knowledge is difficult through
modes such as lectures, textbooks, or manuals. This knowledge type is best transferred
through observations (Szulanzki, 1996; Argote et al., 2003). The type of knowledge under
consideration is essential in understanding to how such knowledge is shared. For example,
sharing explicit knowledge is easier via developmental and formal training than tacit
knowledge.
Polanyi (1996) argues that tacit knowledge is not a separate from knowledge and that
such a form of knowledge is critical in knowledge integration mechanisms. Some
researchers disagree with Polanyi and suggest that knowledge can be categorized into
tacit and explicit forms (Jasphapara, 2004; Mooradian, 2005). Dholakia et al. (2002) states
that explicit knowledge is easier to codify formally than tacit knowledge. Reportedly, 90%
of people’s knowledge is tacit knowledge (Wah, 1899; Lee, 2000). Swap et al. (2001) focus
on the sharing of tacit knowledge and suggested that it is perceived as a more critical form
than explicit knowledge. Smith (2001) highlights that tacit knowledge is vital in attracting
and retaining talented, loyal, and productive workforce. Huang et al. (2011) state that
sharing tacit knowledge frequently occurs in informal situations between individuals in
close relationships. Next, the formal and informal nature of knowledge sharing methods
(Smith, 2001) is discussed in the next section.
19
Nonaka and Takeuchi (1995) proposed the socialization, externalization, combination, and
internalization (SECI) model, a framework for developing methods to convert tacit
knowledge into explicit knowledge and vice versa in a continuous and cyclical manner.
This model consists of four modes of knowledge transformation (See Figure 2.2 for
details). Socialization is about sharing experiences through informal or social interactions.
Externalization is when an individual gains knowledge through formal and codified forms
such as written manuals or through information technologies. Combination occurs when
explicit knowledge gets converted to codified or systematic sets of knowledge through a
range of sources. Internalization occurs when explicit knowledge is modified internally by
an individual, often involving interaction with aspects of the individual’s tacit knowledge
with the new explicit knowledge received.
Figure 2.2 The SECI model (Nonaka and Takeuchi, 1995, p. 80)
Managing such knowledge is crucial for developing strategic resources (Jones, 2003; Lee
and Yang, 2007b).
20
2.2.1 Knowledge sharing processes and its impact on firm performance
Although knowledge is widely collected and held through individual transmissions
in a firm, knowledge sharing is an essential process of KM, as it is the flow and
application of knowledge rather than its stock that creates opportunities for
sustained competitive advantage for a firm. Eisenhardt and Santos (2002) find
that the systematic promotion of knowledge sharing implementation is critical for
successful implementation of KM. Hsu (2008) highlights that knowledge sharing
also supports innovation strategies. Knowledge sharing occurs through formal
and informal means. For example, structured and explicit forms of knowledge can
be transferred through formal knowledge sharing (Alavi et al., 2005; Leonard-
Barton, 1995; Zahra et al., 2006). Similarly, unstructured or tacit forms of
knowledge is collectively held by individuals and is transferred through informal
knowledge sharing mechanisms (Lave and Wenger, 1991; Nonaka and Konno,
1998; Orlikowski, 2002). Both these approaches are highly relevant in attracting
and retaining talented, loyal, and productive workforce (Smith, 2000).
To enhance knowledge-sharing performance and avoid repeating the same
mistakes, firms should share their learnings, experiences, information, and
knowledge by implementing KM strategies. Successful KM strategies can
determine a firm’s and long-term sustainable competitive advantage (Leonard-
Barton, 1995; Drucker et al., 1998; Hooff and Ridder, 2004). It has been widely
noted in the extant literature that knowledge sharing requires individual
employees engaging in behaviors that are conducive to knowledge sharing
(Ardichvili et al., 2003; Ipe, 2003), as it is nearly impossible to competently record
all knowledge (Bhatt, 2001; Horwitch and Armacost, 2002; Rooke and Clark,
2005). As noted in Witherspoon et al.’s (2013) meta-analytic review, individual
21
employees’ attitudes and intentions are regarded as vital in their knowledge
sharing behaviours. Furthermore, informal approaches to knowledge sharing
relies extensively on a trust-based environment that can be collectively created
by an organisation’s employees, leaders and managers (Davison, 2013; Koskinen
et al., 2003). Knowledge as information possessed by individuals consists of
expertise, facts, judgements, and ideas relevant to the performance of
individuals, teams, and firms (Alavi and Leidner, 2001; Bartol and Srivastava,
2002). Tacit knowledge and competitive advantages can often be adversely
impacted when employees leave or retire (Reid, 2003; Sheehan et al., 2005;
Tsoukas, 1996). Knowledge sharing in organizations generally focuses on
communicating and transferring knowledge explicit forms of knowledge from
individual into tacit forms for its productive use. Individuals may also exchange
knowledge through discussions or social interactions to develop new knowledge
(Abudullah et al., 2009; Van den Hooff and De Leeuw van Weenen, 2004).
Based on the review of literature, it is apparent that knowledge sharing research
is grounded in theories of knowledge integration and creation (Alavi and Leidner,
2001; Grant, 1996; Nonaka and Toyama, 2003; Nonaka and Takeuchi, 1995;
Jasphapara, 2004). A number of studies have indicated that reasons affecting
knowledge sharing include reasons such as leadership, management, training for
workers, incentive system, and trust (Chua et al., 2004; Van den Hooff and
Hendrix, 2004; Wong and Aspinwall, 2005; Yang, 2007).
Nonaka and Takeuchi (1995) examined knowledge sharing processes and found
that knowledge creation can be enhanced by the proper attitude of people
towards knowledge sharing. Developing a knowledge sharing culture facilitates
knowledge generation, which helps firms to survive in today’s competitive
22
environment. Thus, there is a need to consider organisational practices such as its
HRM practices that may facilitate in creating a culture that is conducive to
knowledge generation, sharing and integration into an organisation’s daily
productive routines.
Knowledge sharing has several benefits that have been identified in the
literature. For example, Alavi (1999) demonstrates that knowledge sharing
enables employees to contribute to knowledge application and innovation and
ultimately to a firm’s competitive advantage. Knowledge sharing between
employees and across teams allows the capitalization of knowledge-based
resources (Cabrera and Cabrera, 2005; Damodaran and Olphert, 2000; Davenport
and Prusak, 1998). However, it is not always easy to share knowledge. Ardichvili
et al. (2003, p. 70) explored knowledge sharing processes and confirmed that it
can be curtailed by the “fear of criticism” and “fear of misleading others”. The
perception of a virtual community of knowledge sharers actively motivates
individuals to share knowledge (Ardichvili, 2003; Chiu et al., 2006; Wenger et al.,
2002). Despite evidence that knowledge sharing positively contributes to
innovation, motivating people to share has been noted as a key challenge for
reasons outlined above (Ford and Chan, 2003). Appropriately implementing
knowledge sharing is therefore important for firms in a dynamic business
environment to succeed (Kedia, Harveston, and Triandis, 2002).
Firms that can generate and manage unique knowledge tend to create
sustainable and inimitable competitive advantages (Barney, 1991; Grant, 1991;
Lank, 1997). Sharing the best practices within an organization can also influence
the ability of the organization to create these advantages (Szulanski, 1996). The
value of knowledge can be expanded through appropriate knowledge sharing, as
23
it enables improvements in work quality, problem-solving and decision-making
skills (Alavi and Leidner, 1999). By creating and sharing knowledge faster than
competitors, firms can develop competitive advantages every day (Gupta and
Govindarajan, 2000). Despite the acknowledged benefits, knowledge sharing still
remains one of the greatest challenges of KM as employees are often unwilling to
share their knowledge and expertise (Issa and Haddad, 2008). Mitchell (2003)
notes knowledge creation if often as a result of effective knowledge sharing and
that ineffective KM practices may adversely impact a firm’s competitive
advantage (Sarvay, 1999).
2.3 Research on Family-controlled businesses
In the literature on FCBs, there is a clear distinction between FCBs and non-FCBs
populations. Unlike non-FCB firms, FCBs are run and operated by its family
members. Sit and Wang (1898) found that a significant part of management
decision-making falls on families in small to medium-sized Chinese firms in Hong
Kong. In the present research, a FCBs are defined as a business in which the
majority of management stake lies in the hands of a family and its family
members are directly involved in the workings of the firm. Lansberg (1988)
highlights that by not carefully engaging in succession planning at various levels in
a FCB, the overall performance of the FCBs comprising of owners, family
members, and managers will be adversely affected. FCBs should be treated as a
system (Greenberg, 1977; Kantor and Lehr, 1975; Wertheim, 1973) that has
interdependencies and interrelationships between the key decision makers. Dyer
(1986) advocates that all families follow patterned roles as means of interacting
with the explicit and implicit rules that have been created over the years through
24
their family culture. This section reviews the literature on FCBs to identify
concepts that are relevant to studying Chinese FCBs in the context of the HKCI.
Generally, the symbolic management of the members of FCBs and the culture in
such firms is largely shaped and pursued by family members of the FCB unit. The
wealth and knowledge thus created needs to be transferred on to the next
generation for making the business potentially sustainable across generations
(Chua et al., 1999; Molly et al., 2010). The features and cultural characteristics of
FCBs are defined by paternalistic values (Chirico and Nordqvist, 2010).
Turner (1980) found that 93% of factory workers and nearly 70% of employees in
Hong Kong trust the leaders and managers of FCBs. Nearly 70% of those workers
also agreed that there exists teamwork between management and workers.
Redding (1989) concurs with Turner (1980) and notes that managing family
interest is the top priority of FCBs because of “family obligations” as the FCB is
often viewed as a “family possession.” Chinese FCBs tend to recruit less
competent relatives compared to more capable professional managers because
of the need to care for family members is embedded in part of their family’s
obligations and culture. This approach does not always effectively deliver on the
business goals and performance.
Furthermore, in traditional Chinese culture, promotions are based on seniority
(i.e., age) rather than merit. Rewarding seniority conveys loyalty and
commitment (Redding, 1979, 1984). In Hong Kong, the new generation of young
executives also view seniority as an important factor for loyalty and status in
clothing businesses (Redding, 1984; Chiu et al., 2002). Through a sustained
25
research program on the characteristics of typical Chinese-owned firms (Redding,
1979, 1984) summarized the following key characteristics:
1) centralization of power through the boss;
2) friendly relationships with suppliers and customers;
3) high flexibility;
4) minimal management control on individual performance;
5) quick decision-making;
6) limited reliance on logical analysis and rationality;
7) patronage systems; and
8) informal organization structure
Chinese FCBs in Hong Kong typically manage people using belief systems, such as
having non-rational forms of control and where feelings of senior management
are given greater priority (Redding, 1990, p. 42). Such a management style is
more autocratic than in the past (Redding & Richardson, 1986), though now it has
been gradually heading toward more of a decentralized approach. The
management system of FCBs is largely informal, loosely structured and based on
the interpretation of its managers and employers (Redding, 1979, 1984).
Carney (2005) and Sharma et al. (1997) state that Chinese FCBs focus on people,
highlighting the management of relationships. The level of organizational control
required at different stages in an organization’s life cycle varies. Successful
transitions of control are according to phases of expansion, management
succession, transitions of FCBs into public limited firms, or affected by changes in
the external environment. These factors affect control systems in Chinese FCBs.
Zuo (2002), further states that the Guanxi orientation in relation to Chinese
26
culture focuses on developing harmonious relations with each other for
maintaining a strong identity of Chinese FCBs (Dholakia, 2002).
While adopting a transactional approach is essential in achieving control-oriented
outcomes (Howell & Avolio, 1993; Sameroff & Mackenzie, 2003), internal changes
can be modified for reducing transactional conditions, thereby resulting in a
demand for different degrees or emergence of a hybrid system if control in Hong
Kong. Such hybrid systems reflect aspects of both the Western and Chinese
culture to satisfy the social needs while at the same time maintaining the family
dominance of FCBs.
2.3.1 FCBs and knowledge sharing
Macneil (2001) proposes that the executives of FCBs must engage in
organizational learning process as knowledge sharing is becoming increasingly
relevant in organisations (Ardichvili et al., 2003; Hsu et al., 2007). Knowledge
sharing within FCBs can yield both positive and negative outcomes.
Benefits can only follow of there is a positive perception by senior managers, who
must demonstrate a behaviour of encouraging staff to undertake knowledge-
sharing for creating and maintaining a positive knowledge-sharing culture in
organizations. Carney (2005) suggests that FCBs can further enhance their
knowledge-sharing performance and motivate their staff by making appropriate
investments in their technological infrastructure. This is especially true if the
knowledge to be shared is explicit and codified in nature. The leadership style is
also noted as a critical factor in motivating staff to share knowledge effectively
(Sull and Wang, 2005).
27
Sharing tacit knowledge need to face-to-face, unstructured, and personalized
exchanges (Orlikowski, 2002). This type of knowledge is not easy to express and
define; it is best transferred via informal forms of direct showing and common
practices between employees. Informal social interactions may share knowledge
by opportunities, which can help FCBs to build technological capabilities in
current running (Zahra, 2007). Although non-codified and tacit knowledge is
difficult to share, the strong relational ties that this creates in FCBs may provide a
powerful and sustained informal knowledge sharing mechanisms (Sirmon and
Hitt, 2003; Zahra, 2006). Participating in knowledge sharing by FCBs from early
stages helps family members develop deep firm-specific tacit knowledge. Such
knowledge is emphasized in relation to its centrality for a firm (Grant, 1996;
Cabrera-Suarez et al., 2001). In the context of this study, it is worth emphasizing
that Chinese cultural norms will tend to utilize more informal and personal means
for achieving the above outcomes of knowledge sharing (Burrows et al., 2005)
There are several shortcomings that most FCBs face. Despite the importance of
knowledge sharing, several characteristics of FCBs potentially inhibit the
knowledge sharing processes. Being closely held businesses that FCBs are, often
the most valuable information resides in an individual or a few closely associated
family members. Within-family rivalries also can make senior family members
reluctant to share knowledge with the successive generations (Lansberg, 1999).
Additionally, over a period of time family members may also lose interest in the
business or have no passion to teach their offsprings (Grote, 2003; Le Breton-
Miller et al., 2004). These inhibitions are usually caused by a paternalistic
approach, a cultural characteristic of FCBs (Chirico and Nordqvist, 2010). Some
FCBs tend to develop cultures that make their firms inflexible and resistant to
change (Hall et al., 2001).
28
Family members may not usually possess the same level of entrepreneurial spirit
(Morck and Yeung, 2003), and this reason highlights the commitment for greater
levels and efficient practices of knowledge sharing (Cabrera-Suarex et al., 2001).
Rivalries among family members can complicate this sharing of knowledge
(Gomex-Mejia et al., 2001). Jealously, is another factor that is common happens
in family and even non-family members, and this factor is usually induced by the
ambition to have another person’s position in a firm. These are conflicts can stifle
communication (Grote, 2003) and limit the extent of knowledge sharing. Chinese
FCBs are characterized with a strong identity and an informal business setting,
often adopting a paternalistic leadership style. Informal situations are common in
FCBs and helps in fostering close relationships with managers and peers. The
presence of these factors may facilitate or mitigate the knowledge sharing
processes. Researchers argue that emotional involvement and use of private
language in FCBs enhances the communication between family members; this
distinctive approach explains the efficient sharing of knowledge in FCBs, which
often results in better outcomes as compared to non-FCBs (Chirico and Salvato,
2008; Chirico and Nordqvist, 2010).
2.4 AMO: The Performance Rubric
Several studies have focused on factors that impact high performance of an individual’s
performance, namely: their ability (A), motivation (M), and opportunity (O) or what has
been popularly noted as the AMO model (Blumberg and Pringle, 1982; Bailey, 1993).
Blumberg and Pringle (1982) introduced “opportunity” in the AMO framework for creating
a cooperative environment, one which encourages people to share their knowledge with
other employees. While motivation also influences opportunity and ability; a firm that
29
generates a highly supportive climate for knowledge application and sharing may drive
employees toward further knowledge and skills development (Ryan and Deci, 2000).
The AMO model has been extensively used for understanding individual level differences
in performance in an organisational context (e.g., Boxall and Purcell, 2003; Waldman and
Spangler, 1989). Other studies offer a wide variety of contexts and situations leading to
high performance (Aguinis et al., 2015). For example, subsidiary performance can be
improved by leveraging firm-level expatriate competencies (Chang et al., 2012) and
Appelbaum et al. (2000) indicate that employee performance is the function of ability (A),
motivation (M), and opportunity (O) enhacing practices. Ability can be improved if the
selection or initial training of employees for advanced or job-specific skills and knowledge
is provided for by the organisation. Workers who are skilled and able then need to be
motivated to become efficient, often using a combination of some intrinsic and extrinsic
incentive systems, such as employment security, performance-related pay, internal
promotions, and training investments (Appelbaum et al., 2000). Finally, the willing and able
workers tend to perform better when they have the opportunity to apply their skills and
motivation to a given work context. Thus, working arrangements can provide employees
with the opportunity to influence the decision-making process of a firm and motivates
them to share their task-specific knowledge through an environment such as that of trust
(Appelbaum et al., 2000). The AMO model has since been adopted by many researchers to
study individual-level performance outcomes for various aspects of an individual’s
performance (Jiang et al., 2012; Wolters, 2014)
Many scholars have also advocated the adoption of the AMO model at organizational level
for analysis, particularly in areas of strategic decision making and human resources
management (Black and Boal, 1994; Lepak et al., 2012; Wu et al., 2004).
30
In general terms, ability is considered an important factor in knowledge sharing because
the competence of an employee is a core requirement for high work performance. Ability
comprises of knowledge and skills of workers. Knowledge can be viewed as the intellectual
capital of individuals that can be applied to work tasks. Subramanian and Youndt (2005)
highlight the importance of skills for individual work performance and functional expertise
of employees. Researchers often measure abilities in terms of human capital and
educational levels. For example, Lepak et al. (2012) measured ability in terms of human
capital and training, whereas Coff (2002) states that human capital is a combination of an
employee’s knowledge, skills, and abilities. Overall, training is the key to ensuring that
employees have the requisite skills for completing various tasks in relation to the tasks they
need to perform. Becker (1964) claims that by investing in training that improves firm-
specific skills, productivity can be directly increased.
Motivation is the intensity, direction, and duration of employees’ effort toward work
activities for achieving high performance (Campbell et al., 1993). Jiang et al. (2013)
indicate that motivation reflects employees’ willingness to exert effort at work. For
knowledge sharing to work, employees must be motivated to voluntarily participate in
such activities. Motivation as we know can be intrinsic, extrinsic and must also be
mutually beneficial (Adler and Kwon, 2002). Payment and monetary rewards are strongly
linked to the AMO model as they are incentives that are part of overall payment systems
(Boxall et al., 2009; Purcell and Kinnie, 2007). Incentive systems also relate to group work
and collaboration (Alder and Kwon, 2002). Intrinsic motivation is related to the aspiration
to do something pleasant for the staff, and it provides a sense of organization, fulfilment,
and confidence that can drive employees toward the target (Huselid, 1995). Job
31
satisfaction is a positive emotional state that also affects an individual’s appraisal of a job
experience or the actual job and their motivational levels (Locke, 1976).
Opportunity refers to an environment that enables employees to effectively apply their
skills and motivation to perform their tasks well (Jiang et al., 2012). Bowen and Lawler
(1992) advocate empowerment is a key resource to instill decision-making power in
employees to influence the firms’ direction and task performance through knowledge
sharing. Conversely, Wolter (2014) finds that the best way for employees to perform and
accomplish their tasks is to work independently and employees should be given the
opportunity to demonstrate their abilities by contributing to firm performance. Other
studies have found that trust, knowledge sharing, and communication can drive employee
motivation (De long, 1997; Ismail et al., 2007). Trust-based relationships lead to building
trust within teams (Black and Boal, (1994, p. 7) state that the performance outcomes of a
unit depend on “the interactions among the capacities of unit members, the motivation
present, and the unit’s physical and capital resources.” Boudreau and Ramstad (2002)
claim that employee skills, motivational elements, and value-creating conditions are
crucial for human resources to create organizational value. Although performance varies
across contexts, it is overall a function of ability, opportunity, and motivation (Boxall &
Purcell, 2003; Waldman & Spangler, 1989). Some researchers disagree with the
mechanisms through which these factors operate (Siemsen et al., 2008) and have
suggested that these elements should be grouped differently across various performance
contexts. In most job settings, employees must possess minimal levels of motivation and
ability and be given opportunities to contribute to firm performance (Schwab and
Cummings, 1973; Siemsen et al., 2008).
Thus, strengthening training, motivation, and trust is the key to building and developing
knowledge within an organization. Investment in training and development enhances
32
individuals’ human capital and firms’ absorptive capacity (Jerez-Gomez et al., 2004).
Rewarding new skills and knowledge creation motivates individuals to experiment with
novel ideas, leading to new knowledge creation (Jerez-Gomez et al., 2005b; Lawler et al.,
2001). Teams and cross-functional collaborations have been found to promote knowledge
sharing (Appelbaum and Gallagher, 2000; Lepak et al., 2007), which supports the
exchange of knowledge at a group and organizational level (Garvin, 1993; Goh and Ryan,
2002). Opportunities to collaborate with others in small groups make jobs inherently
satisfying to employees, especially when they can willingly choose to participate in such
environments (Wood and Wall, 2007). Cooperative and collaborative work environments
may also increase the ability by allowing knowledge sharing among employees (Kwon and
Alder, 2002). Teamwork can support all the three factors of the AMO model. Autonomous
work teams create opportunities for participation and team-based organization also
fosters motivation among team members (Kwon and Alder, 2002). Having explored the
literature on AMO factors and its impact on performance and knowledge sharing in
general, the following section reviews the literature that focuses on the specific impacts of
each of the AMO factors on knowledge sharing. While there are several mechanisms and
HRM practices for enhancing ability, motivation and opportunity, this study reviews the
key HRM practices that will then be included in the study’s guiding framework for testing
and analysis.
Overall, employee abilities, motivation, and the opportunity to perform to their potential
can lead to good performance outcomes (Becker and Huselid, 1998; Delery and Shaw,
2001, Guest, 1997; Jiang et al., 2012) at both employee and organizational level. Combs et
al. (2006) argue that a high performance work systems (HPWS) significantly affects firms
more than employees’ individual capacity. In their bundle of (HPWS), Combs et al. noted
commitment to employee training investment, effective incentive systems, successful
work system, and a participative management structure was found to provide employees
33
with opportunities to contribute. Shih et al. (2006) also found training, motivation, and
involvement as leading contributory factors for performance improvement. Training
programs, job empowerment, and incentive arrangements can boost firm performance by
enhancing motivation (Shih et al., 2006). Sustainable competitive advantages of firms
embedded in employee behaviour are hardly inimitable (Lade and Wilson, 1994), and firms
can effectively nurture the desired employee skills for improved performance (Shih et al.,
2006). The following section provides a detailed review of literature connecting knowledge
sharing with ability, motivation and opportunity factors.
2.5 FCBs and AMO model
2.5.1 Ability and knowledge sharing
Ability is the application of a set of related competences for completing one’s work. For
example, the effectiveness of line managers may decline because of inadequate training
(Whittaker and Marchington, 2003; Boxall and Purcell, 2011). Bailey (1993), following
Applebaum et al. (2000) conceptualized the AMO model as a high performance work
system. Adopting a systems view of HRM system is a useful way for analyzing the impacts
of employees’ abilities (A) motivation (M) and opportunities (O) to partake in productive
organisational activities (Khodabakhshi and Abbasi, 2015).
Training is essential for developing employee ability, as it increases skills, expertise, and
awareness (Renwick and Redman, 2013). Lack of training affects employee performance
because in the absence of expert knowledge (Hall and Torrington, 1998; Lowe, 1992)
employees are likely to perform sub-optimally. Lack of experience and knowledge on
information needed in performing responsibilities results in limited human management
skills (Grimshaw et al., 1997).
34
Additionally, a firm need to “recognize, learn and also utilize knowledge from the
surroundings” (Cohen and Levinthal, 1989, p. 13). A firm can utilize knowledge from
external sources, depending on the ability of the firm to tap into such knowledge and have
in place mechanisms that support knowledge transfer across individuals, functions, and
departments. Educational levels alone do not significantly influence the ability to exploit
inter- and intra-industry knowledge (Schmidit, 2010), firms should be adept in integrating
such knowledge through a range of HRM and management practices (Malik & Nilakant,
2015).
Training also enhances the self-efficacy levels of managers and peers through successful
experience and coaching (Bandura, 1997). Stakeholders then become confident to share
their abilities and knowledge with others. The critical roles of different types of training
vary. Team building training increases the levels of structural and relational social capital,
thus encouraging knowledge-sharing behaviours (Cabrera and Cabrera, 2005; Currie and
Kerrin, 2003). Cross-training enhances interactions among employees and awareness in
different jobs and departments (Cabrera, 2005: Cabrera et al., 2006). Socialization
programs help share norms and identity with others, through which trust can be built
(Kang et al., 2003). Many studies found that training is a tool that helps managers and
peers to use organisational systems effectively with minimal cost (Cabrera and Cabrera,
2002; Cabrera, 2005). Training also encourages knowledge sharing behaviours (Hislop,
2003; Oldham, 2003; Zarraga and Bonache, 2003), enhancing the knowledge, abilities,
skills, and positive attitude of stakeholders to develop sustainable competitive advantages
of firms (Noe, 2010; Vodde, 2012).
Motivation to attend training also affects the attitude towards training and its
effectiveness (Machin and Treloar, 2004; Noe, 2010). The AMO model does not represent
35
either a fully interactive or fully additive effect of this, however, there is research that
points to effective transfer of training. Central to the training transfer literature, among
other factors is the importance of combination of opportunity and motivational factors
that may exert an additive effect for ability; however, each part does not exert an effect
on its own. Opportunity or motivation may add to the information employees need to
perform their responsibilities and may assist employees with limited HRM skills (Grimshaw
et al., 1997). Systematic and continuous approaches to training are therefore necessary in
organizations (Cunningham and Hyman, 1999; McGovern, 1999). Overall, the focus on
employee training is a core HRM practice in the AMO paradigm that is likely to have an
impact on knowledge sharing performance.
2.5.2 Motivation and knowledge sharing
While employee training is critical in developing employees’ ability, the knowledge thus
developed needs to be shared through appropriate employee motivational mechanisms.
Organizational commitment can motivate employees to share their own knowledge
(Hislop, 2003).
Central to the AMO paradigm is “eliciting discretionary efforts from workers,” which is
often regarded as “eliciting staffs’ knowledge.” (Salis and Williams, 2010, p. 6). These
efforts are usually viewed as combining resources to attain competitive benefits. Given
the widespread use of knowledge in companies, a lack of attention to the knowledge
construct may lead to aggravated situations (Salis and Williams, 2010).
Incentive systems and employment security may provide employees with the necessary
motivation to share knowledge within the organisation. These elements indirectly impact
employees to share their knowledge with others. Implicit and explicit knowledge may be
36
combined to carry out changes in the workplace and undertake problem solving in teams
or groups for productivity advantages, which may accrue from the exchange of both
knowledge types (Salis and Williams, 2010).
Incentive systems inspire employees to share their knowledge within the
organization (Govindarajan and Gupta, 2006; Lin, 2014; Aspinwall and Wong,
2005). An important element of a successful incentive system is encouraging
employees to exchange knowledge. Silverstein (2010) demonstrated that
recompense may be in the form of intangible rewards and financial inducements,
such as job satisfaction. Employees often share their knowledge to be
recompensed. Szulanzki (1996) recommends that people are unwilling to share
their knowledge because of insufficient motivation along with the exclusive
nature of recompense. Therefore, firms always adopt constructive tools of
incentive systems to advance the readiness of knowledge sharing within the
organization.
Davenport and Prusak (1988) asserted that knowledge sharing should be
encouraged and rewarded based on the structural performance requirements of
individuals in firms (VonKortzfleish and Mergel, 2002, p. 246). Firms create a
compensation system with either monetary or non-monetary incentives to
reward such behaviour (Davenport and Prusak, 1998; Hargadon, 1998). Such
incentives and motivational rewards can satisfy the extrinsic and intrinsic needs
of employees (VonKortzfleisch and Mergel, 2002). Formal and informal
compensation approaches vary from extrinsic (e.g., increased salary, promotion,
job security, bonus, and career development) to intrinsic incentives (e.g., praise
and public recognition) (Kankanhalli et al., 2005; Choi et al., 2008). Mergel and
37
VonKortzfleisch (2002) found that knowledge-intensive organizations tend to
focus on cultural changes that generate motivation for knowledge sharing.
Employees often respond to external inducements easily than to intrinsic
inducements (Bartol and Srivastava, 2002). However, as Bock, Lee, Kim, and
Zmud (2005) assert, external inducements may negatively affect the intention
of employees toward knowledge sharing. Firms need to create long-term
incentive systems to allow continuous knowledge sharing (Davenport and
Prusak, 1998; Choi and Cheng, 2005) and prevent employees from misusing
the compensation systems (Chong, 2006). Companies can also recompense
employees for participating in knowledge exchange aimed at future
development and training (Gumbley, 1998).
Although some firms may be doubtful about the effectiveness of a recognition and
incentive system for driving workers to share knowledge, affiliating reward and
recognition with knowledge sharing can at least highlight the significance of knowledge
sharing to all employees (McDermott and O'Dell, 2001). Wasko and Faraj (2000) argue
that physical inducements may motivate knowledge storing and contentious actions may
decrease knowledge flows among employees.
Significant changes in incentive systems are required to encourage employees
to share their knowledge (Bartol and Srivastava, 2002). Changes in incentive
systems often result in changes in an organization’s culture (Harman and
Brelade, 2000). Hargadon (1998, p. 225) recommends companies to adopt a
common culture that “shows the readiness of members to search for others
varying knowledge and to share their own; it can best be condensed as an
attitude of wisdom.”
38
Rewarding knowledge sharing is critical for motivating individuals to transfer knowledge,
especially in Western management systems (McDermott and O’Dell, 2011). In Chinese
culture and FCBs, knowledge is managed informally, and knowledge sharing relies on trust
and close relationships among stakeholders (Burrows et al., 2005). Overall, the presence of
incentive systems as core HRM practices in the AMO paradigm is likely to have an impact
on knowledge sharing.
2.5.3 Opportunity and knowledge sharing
Workplaces can attain trust and share knowledge by creating social networks
comprising of individuals with strong bonds and corresponding levels of trust
(Lepak & Snell, 2007). Tsoukas (1996) found that a firm is a “distributed
knowledge system,” in which knowledge is dispersed individually and situated in
the social interactions of employees in different parts of the organisation.
Trust is characterized as the eagerness of a party to be accessible to the actions
of another party situated on the anticipation that the other party may perform a
particular action important to the trust or, regardless of the ability to control and
monitor the other party (Davis & Schoorman, 1995). Trust is developed through
meaningful and recurrent communications, where individuals learn to be free,
comfortable, and open in sharing individual insights and concerns. Further, trust
can be fostered where ideas and assumptions can be challenged without the risk
or fear of consequence, and where various opinions are prioritized over
compliance and commonality (Holton, 2001). To enable knowledge sharing, trust
should exist in three dimensions, namely, capability, honesty, and kindness
39
(Usoro et al., 2007). Although it may take time, high levels of empathy and trust
among individuals is key to an effectual learning climate (Bogenrieder and
Nooteboom, 2004).
Buckley and Strange (2011) assert that trust plays an important role in conflict
mitigation, constructive knowledge sharing, and cooperative knowledge sharing
behaviours in firms. Zaheer and Zaheer (2006) discover that trust needs
institutional and cultural support, but their finding varies in three countries
(Japanese, Japanses-Americans, and Americans).
Trust involves having reciprocal faith in others (Kreitner and Kinicki, 1992).
Knowledge sharing cannot be performed with only honest trust. Nahapiet and
Ghoshal (1998) regard trust as an essential factor of social capital that is
immersed in human relationship networks. Zhang and Cohen (2007) contend that
employees need capacity and time for meeting and working closely together for
developing trust and mutual understanding.
Although trust is not identified as an individual element that affects the readiness to share
knowledge (Ding et al., 2007), it trust can increase compassion among employees (Cross,
Abrams, Lesser, and Levin, 2003) and their readiness to collaborate (Tyler and Kramer,
1996). Davenport and Prusak (1998) assert that trust must be conspicuous and alluring and
must emanate from the top management. When employees recognize the presence of high
trust in their relationship, they are likely to be interested in participating in knowledge
interactions and social exchange (Levin and Cross, 2004). Knowledge sharing without trust
is impossible because employees refuse to share their knowledge with those who they do
not trust (Ellis, 2001; Van Wijk et al., 2008). Knowledge donators only share knowledge
when they trust the knowledge collectors (Issa and Haddad, 2008). Knowledge donators
40
must trust that their shared knowledge will be used appropriately, and knowledge
collectors should believe that their collected knowledge is the best available knowledge
(Buckman, 1998). Overall, by focusing on creating trust as a core HRM practice in the AMO
paradigm, trust is likely to have an impact on knowledge sharing performance. The
following section reviews the relationship of FCBs with the AMO in general and then
examines the specific impacts of AMO factors in the context of FCBs. The above studies
offer adequate support for employing the AMO paradigm for this study.
2.6 FCBs and AMO model
Teamwork, leadership, and incentives are contextual factors influencing creativity
(Shalley et al., 2004). The AMO model is developed for analysing high
performance of employees in an organisational context (Delery and Shaw, 2001).
Considering its critical role in sustaining participatory innovations, leadership can
build trust and personal relationships, especially in FCBs (Chrisman, 2007; Zahra,
2004). Leadership from senior managers can be viewed as constructive in driving
key changes. Management determination changes the organizational cultures to
match the new ways of working in a way that managers and employees are
accepting of change (Cox, 2013). Leadership is also critical in facilitating
connections at all levels of a company; right form the strategic issues to daily
operational matters leaders must ensure that employees are briefed and
consulted about the changes in FCBs. Leaders should share knowledge on work
performance openly with workers to demonstrate business situations. If such an
approach is evident at a workplace, employees may be willing to accept changes
to their terms and conditions even in adverse financial circumstances (Cox, 2013).
41
Firoz and Chowhury (2013) argue that strategic leadership can ensure a profitable
and high performance work strategy by incorporating AMO approaches in a firm’s
architecture of work practices. Using such an approach, future leaders can be
trained and initiatives can be developed to bring about the highest levels of
organizational efficiency. Organizations can also link their compensation
packages successfully with performance management systems and keep their
employees motivated. Employees are entitled to receive competitive salary
packages inclusive of attractive bonus, insurance facilities and health care, paid
leave, loans, incentives, disability benefits, and opportunities for career
progression (Firoz and Chowdhury, 2013).
2.6.1 FCBs and AMO
Development and training plays a role in assuring development of skill levels for
performing key roles and tasks related to a job and involvement in various teams.
High-performing organizations tend to draw out the skills of their employees and
encourage employees to perform organizational goals (Belanger et al., 2002). By
moderating the relationship between HR practices that enhance the
opportunities of employees and organizational citizenship behaviours in FCBs, the
effect on environment increases. (Khodabakhshi &, 2015; Tzafrir, 2005).
Knowledge sharing is a series of actions through which people exchange their
knowledge. Individual knowledge is converted into organizational knowledge,
and the opportunity to practice and learn new knowledge, experience and skills is
enabled through knowledge sharing (Kogut and Zander, 1996; Hughes, 2005).
42
According to knowledge-based theories (Hsu et al, 2007; Renzl, 2008; Mooradian
et al., 2006) social community has have the skill to create and transfer knowledge
with efficiency and high speed (Fukuyama, 1995). Such theories consider that
creating and transferring knowledge requires positive relationship among
organisational members. Therefore, social capital is needed in the social
networks within companies and can be assumed to be an essential asset in
maximizing organizational advantage (Lesser, 2000).
2.7 Research gap, key questions and hypotheses development
2.7.1 Research Gap
Overall, based on the review above, this thesis argues that knowledge sharing is driven by
three key enablers, namely, ability, motivation, and opportunity (Becker and Huselid,
1998; Delery and Shaw, 2001; Guest, 1997, Shih et al., 2006) and the moderating effect of
FCBs (Goh et al., 2012). Although extant literature on the practice of knowledge sharing in
business exists, studies on FCBs in the clothing industry in Hong Kong are lacking. Zahra et
al. (2007) conducted a similar empirical study in the US about the FCBs as a moderating
factor in knowledge sharing and technological capabilities, but they focused on the
relationship between the types of formal and informal knowledge and that technological
capabilities are moderated by FCBs. An in-depth study on how the critical AMO factors
(training, incentive system, and trust) influence the performance of knowledge sharing is
still lacking and needs exploration for reasons outlined earlier in this chapter.
From the above review of literature on knowledge sharing, the following research gaps have
been identified.
43
Lack of research focusing on AMO factors in context-specific knowledge sharing
The fundamental idea of formal and informal knowledge sharing is that context-specific-
behaviour of leaders, managers and organisational culture is critical in understanding
knowledge sharing (Augier et al., 2001; Ford and Staples, 2010; Nonaka et al., 2000). Further,
FCBs possess a distinctive characteristic that allows it to create a unique cultural
environment; therefore, intensive research is needed to explore knowledge sharing in FCBs.
Lack of knowledge sharing studies in FCBs using the AMO model
With the growing importance of research on FCBs (Williamson, 1999; Mikado, 2003), its
relationship with knowledge sharing needs further examination (Lorenzen and Mahnke,
2002; Miller and Miller, 2005). Furthermore, FCBs in the HK clothing industry have not
been analysed in existing studies on the topic. This is surprising given the major
contribution HKCI makes to the nation’s GDP (HKTDC, 2016). Given that majority of the
clothing manufacturers in Hong Kong are FCBs (HKTDC, 2016), they also possess extensive
experiential knowledge about the products and materials sourcing. If there is little sharing
of knowledge between generations, then the increasing concern of the declining
performance of exports in HK’s clothing industry is likely to get even worse. In view of the
above, this study will examine the effects of AMO model on knowledge sharing and
analyse any moderating effects of FCBs.
Studies on the AMO model as applied to the investigation of management and leadership
is rather limited. Rutherford et al. (2004) point out that the leadership style of FCBs is a
critical success factor and is receiving increasing attention within the organization studies
literature. The AMO model could thus be explored in the context of HKCI, especially in
44
relation to the role of leaders and managers in FCBs have in strengthening knowledge
sharing. FCBs tend to have a specific type of leadership style and may moderate
knowledge sharing capability by identifying the knowledge requirements of the chosen
strategies and creating the ideal conditions for the development and exploitation of such
knowledge.
Different dimensions of learning capability are considered vital in knowledge transfer and
sharing, by creating a culture of experimenting
With the appropriate training and incentive systems, teamwork that is built on trust, and
leadership style that encourages knowledge sharing and open-mindedness (Goh et al.,
2012). Jiang et al. (2012) assert that the underlying AMO factors act in a synergistic
manner (Becker and Huselid, 1998; Delery and Shaw, 2001; Guest, 1997) to obtain desired
employee and firm performance. Shih et al. (2006) identified training, employee
involvement, and motivation as key contributing factors for knowledge sharing. Exploring
the role of FCBs in knowledge sharing practices is interesting as FCBs can understand how
the AMO model can be applied for developing strong knowledge sharing capacity for
creating sustainable competitive advantages for firms operating in the clothing industry in
Hong Kong. The findings can stimulate further empirical studies in different industries in
Hong Kong and other Asian nations that have FCBs operating in industries such as
electronic, toys, and watches. To this end, this research focuses on answering the
following research questions.
2.7.2 Research Questions
1. Does ability (training workers), motivation (providing incentive systems),
opportunity (creating an environment of trust) have a significant effect on
knowledge sharing in the HKCI?
45
2. What are the relationships between FCBs, AMO factors and knowledge sharing in
the HKCI firms?
Therefore, based on the above research questions, the following hypotheses will be tested
in this study:
Hypothesis H1.1:
In the HKCI, training for workers is positively related to knowledge sharing.
Hypothesis: H1.2:
In the HKCI, incentive systems are positively related to knowledge sharing.
Hypothesis H1.3
In the HKCI, trust is positively related to knowledge sharing.
Hypothesis H2.1:
In the HKCI, FCBs act as a moderating factor in the relationship between training for
workers and knowledge sharing.
Hypothesis H2.2:
In the HKCI, FCBs act as a moderating factor in the relationship between the incentive
systems and knowledge sharing.
Hypothesis H2.3:
46
In the HKCI, FCBs act as a moderating factor in the relationship between trust and
knowledge sharing.
2.8 Chapter Summary
This chapter reviewed the extant literature on the various factors known to have an
impact on knowledge sharing and proposes a preliminary guiding framework for analysing
the study’s research questions and hypothesized relationships. The chapter also outlines
the context for the study. The literature review also identifies the antecedents of
knowledge sharing and the potential role of FCBs as a moderator between AMO factors
and knowledge sharing.
47
Chapter 3
Research Design and Methodology
3.1 Introduction
This chapter presents details of the research methodology employed in this study. This
chapter has five sections and begins by highlighting the research questions based on the
review of literature undertaken in the previous chapter. More specifically, it reviewed how
various factors affect the implementation of knowledge sharing. This review informed the
development of the study’s research questions and hypotheses for further investigation. In
the second section, an outline of the research process is followed by descriptions of
quantitative approaches and analytic techniques employed for measuring and collecting the
data. The section also describes the rationale and justification for the use of the chosen
method–survey design–in answering the study’s research questions. The third section
focuses on questionnaire design and the measurement of key constructs. Section four
explains the sampling and data collection procedures employed for this study. This is
followed by a consideration of any ethical issues that may be involved. Lastly, the fifth
section provides details of the analytic procedures employed in this study. These include,
for example, employing Pearson’s product moment correlation, factor analysis, and
multiple regression analysis using Process Macro in SPSS were used to test the study’s
hypotheses. The use of a seven-point Likert scale (1= Strongly disagree; 2= Disagree; 3=
Moderately disagree; 4= Neither agree nor disagree; 5= Moderately agree; 6= Agree; 7=
Strongly agree) for consistent measurement of each item of the construct. Additionally,
demographic data, such as number of staff employed by the firms, management position,
FCBs performance, and businesses category, were also included in the survey
questionnaire. The chapter concludes with a brief summary.
48
Methodological overview
All research methods are based on the underlying assumptions of testing validity and
reliability of the research (Myers, 1997). An illustrative outline of Chapter 3 is shown below
in Figure 3.1.
Figure 3.1. An outline of Chapter 3
3.2 Research Process: Philosophy and paradigms
Employing a scientific approach to a research problem can enable a researcher to
objectively conduct the research. For this reason, the researcher has chosen a research
philosophy and paradigm that supports the researcher’s paradigmatic stance. A paradigm
refers to “a set of basic beliefs (or metaphysics) that deals with the ultimate or first
principles. It represents a worldview that defines, for its holder, the nature of the world,
the individual’s place in it, and the range of possible relationships to that world and its
49
parts” (Guba and Lincoln, 1994, p. 107). A paradigm also refers to a set of philosophical
assumptions that each researcher has and includes approaches such as realism, positivism,
constructivism, and critical theory (Healy and Perry, 2000). For example, a researcher may
choose between positivism and interpretivism as his/her research paradigm; and both are
commonly used as research frameworks (Neuman, 2000). Positivist approach rely on
deduction whereas interpretivism involves induction. Irrespective of the paradigmatic
choices, the limitations of each research approach must be truly presented through the
application of high ethical standards before the research is conducted (Cooper and
Schindler, 2001).
3.2.1 Positivist research approaches
It is important that the researcher clearly states the epistemological and ontological
position employed in her research. Epistemology is defined as “what is regarded as
acceptable knowledge in a discipline” (Bryman, 2008, p. 13). Positivism belongs to the
epistemological belief that social sciences are modelled on natural science approaches, in
which theories are tested by adopting quantifiable measurements and deductive methods.
“[It] begins with a theoretical proposition and then moves towards concrete empirical
evidence” (Cavana et al., 2001, p.35). An epistemological foundation relies on the
preference of relevant research methods (Bryman, 1984; Drisko, 1997). Positivism attempts
to understand the principles of natural scientific research by testing theories
experimentally, in order to determine whether those theories represent reality (Guba and
Lincoln 1994; Kolakowsk, 1993; Perry, Riege et al. 1999).
Meanwhile, ontology is defined as “the nature of social entities” (Bryman, 2008, p. 18).
Different views and perceptions can be produced from quantitative and qualitative
research, which are based on ontological foundations. As a philosophy, positivism adheres
50
to the descriptive method, which assumes that the nature of reality is objectively gained via
a quantifiable observation by conducting statistical analysis. In fact, a positivist paradigm is
independent and aims to remain purely objective. The positivist paradigm employs a
quantitative mode of inquiry to test the hypothesis, which posits that social reality has an
objective ontological structure (Morgan and Smircich, 1980; Draper, 2004).
Positivism is often associated with quantitative analysis methods, such as surveys, statistics,
and experimental designs. The advantages of positivist research include its ability to cover
a wide range of situations; however, it cannot be used to gain in-depth insights into issues
such as human emotions, feelings, and thoughts.
3.2.2 Quantitative Research
Quantitative research is good at dealing with a wide range of variables involving large
numbers of respondents. Furthermore, it is well suited for defining the variables to be
studied and tested through hypothesis generation and prior theories. Such methods are not
only ideal for identifying causalities but also for looking at the significance of findings.
“Reliability and validity are tools of an essentially positivist epistemology” (Watling, as cited
in Winter, 2000, p. 7). The above approach is also high in delivering reliability and validity
of a research (Bryman, 2008; Karami et al., 2006).
Researchers who utilize quantitative or positivism research use quantitative measures and
experimental methods to examine hypothetical generalizations (Hoepfl, 1997) and analyse
causal relationships between variables (Hoepfl, 1997; Denzin and Lincol, 1998).
Quantitative research permits the researchers to accommodate them to be study with the
problem or concept, and develop hypotheses to be tested.
51
Despite the several advantages noted above, quantitative researchers often fail to
distinguish between the elements of human and social institutions from the nature of
reality (Schutz 1962) as such methodologies only generate quantitative results and reduces
information into simple numbers in controlled situations. Unfortunately, such simple
numbers are insufficient in explaining the meaning of human relations and behaviours. The
measurement process may thus provide a spurious sense of accuracy to a proposed
hypothesis. Cross-sectional quantitative research conducted within each sample at a given
points unable to track the individual attitudes and values change over time (David de Vaus
2001, Bryman 2012). Quantitative research also lacks qualitative richness and cannot
explain human-emotions (Skiner et al., 2000) and social problems (Bogdan and Biklen,
1998). The interpretivist paradigm and qualitative research methods are commonly used
for other research needs. This is briefly discussed in the next section.
3.2.3 Justification for a positivist and quantitative methodology
Numerous reasons can be noted for justifying this study’s choice of a positivist approach.
First, the research can be replicated using the AMO model to generate testable hypotheses.
The state of prior studies and theories is mature for allowing the replication logic. The
positivist paradigm has relative strengths as it has established processes to illustrate
replication, generalization, causality, objectivity, and implement scientific measurement,
thus meeting the research objectives. Second, quantitative research analyses the trends
and causes behind a phenomenon. In this case, a deductive quantitative approach is the
best methodology that can be used to achieve results by scientific measurements, which
includes concepts of reliability and validity. These concepts will be discussed later in the
section on measurement and scaling.
Third, the conceptual framework and hypotheses stated in earlier research (Zahra et al.,
2007), is relevant for carrying out a quantitative design for the proposed research of HKCI
52
firms. Fourth, this choice is informed by the researcher’s comfort with the quantitative
paradigm in terms of her epistemological, ontological, and methodological assumptions.
Fourth, the focus of this research is on developing an existing theory through theory testing
(Sarantakos, 1998). Fifth, through the scientific measurement of the key variables of the
Ability, Motivation, and Opportunity (AMO) model (Salis & Williams, 2010) allows for
further replication. Thus, overall, the research design of the current study is based on
established a positivist set of assumptions.
3.3 Research design
Selecting an appropriate research design is important in testing a theoretical framework as
well as for collecting and analysing the data. This quantitative study yields data that can was
further analysed, for example, in terms of the details of the respondents’ firm
characteristics of FCBs in the sample to highlight the meanings derived from the survey
data, using reliable and validated measures, for making numerical interpretations conducts
analysis through statistical methods (Healey and Rawlinson, 1994; Saunders, Lewis, and
Thorn hill, 2009).
An experimental research design incorporates controlled testing of the causes and effects
of the independent and dependent variables (Creswell, 2003). Experimental designs often
have comparison groups and attempt to make groups as similar as possible except in
relation to experimental interventions. Given that the current study is about predictability
of associations between individual factors with knowledge-sharing processes, an
experimental research approach is appropriate for the research objectives of this study. An
experimental research approach also helps eliminate the influence of other variables so that
the effect of the intervention can be clearly seen. However, the rigor of experimental
53
designs varies depending on the contexts within which the experiment is conducted (De
Vaus and de Vaux 2001).
3.4 Research question and hypothesis development
3.4.1 Research question
Informed by review of extant literature, a comprehensive theoretical framework was
developed (Figure 3.2.1) which incorporates aspects of the AMO model and tests its
relationship with knowledge sharing in FCBs within the context of HKCI. This framework
incorporates three main relationships and five key constructs, namely: ability (training for
workers), motivation (incentive systems), and trust (opportunity), knowledge sharing and
FCBs. These relationships form the basis of the study’s key research questions.
Q1 Does ability (training for workers), motivation (providing incentive systems), and
opportunity (creating an environment of trust) have a significant effect on knowledge
sharing in the HKCI firms?
RQ2: What is the relationships between FCBs, AMO factors and knowledge sharing in
the HKCI firms?
54
Figure 3.4.1 Framework with research questions
The development of the research questions and its linkages with the literature is
summarised in Table 3.4 below
Table 3.4: Questionnaire Road Map
Research Question Questionnaire Variables in the study
References from the literature
Q1 Ability (IV) Ability (Training for Workers) Wong and Aspinwall (2005)
Q1 Motivation (IV)
Motivation (Incentive Systems) for knowledge sharing
Wong and Aspinwall (2005)
Q1 Opportunity (IV) Opportunity (Trust) for Knowledge Sharing Mooradian (2006)
Q2 Knowledge Sharing (DV)
Effectiveness of Knowledge Sharing Zahra (2007)
Q2 Family-controlled businesses (M)
Ownership and characteristics of a firm Chua et al., (2004)
Legend: IV= Independent variable; DV dependent variable; M= Moderating variable
55
3.4.2 Hypothesis development
Based on the above research questions, six hypotheses were developed as per details
below. As this thesis explores the concept of knowledge sharing and posits that its
relationship with the AMO factors is likely to be more positive in FCBs than in Non-FCBs
(Chua et al., 2004; Wong and Aspinwall, 2005; Mooradian, 2006; Zahra, 2007; Salis,
2010), these assumptions become a source of inspiration for the study’s research
questions and its associated hypotheses as follows (See also Figure 3.5 below):
RQ1: Does ability (training for workers), motivation (providing incentive systems), and
opportunity (creating an environment of trust) have a significant effect on knowledge
sharing in the HKCI?
To answer this question, the following hypotheses were developed.
H1.1: In the HKCI, Training for Workers is positively related to Knowledge Sharing.
H1.2: In the HKCI, Incentive Systems are positively related to Knowledge Sharing.
H1.3: In the HKCI, Trust is positively related to Knowledge Sharing.
Zahra et al. (2007) suggest that FCBs are better at implementing knowledge sharing
compared with Non-FCBs. This is because they foster an environment that motivates
employees to develop and enhance their technological knowledge through the firm’s daily
management practices. FCBs also complement their knowledge by providing
opportunities to share and transfer knowledge across generations. For example, Salvato
(2008) advocates that knowledge sharing in FCBs influences motivation, organizational
commitment, and social interactions within these firms. An FCB has the flexibility to
respond and react to volatile contexts of business, particularly when the formation and
rigidity, which is usually found within bureaucratic in Non-FCBs, interferes with the
56
practice of knowledge sharing among employees (Ding, Zhang, and Zhang, 2008; Miller
and Miller, 2005). Practitioners and scholars interested in FCBs have explored new
knowledge and insights into the causal processes that underlie in these firms (Lewin,
1940). Many researchers have focused on the concept that FCBs have an impact on
knowledge sharing (Sharma., 2004; Zahra. 2004; Zellweger et al., 2010). Knowledge
development helps create conceptual frameworks that stimulate our understanding of the
phenomenon under study (Sutton and Staw, 1995). This process leads to the study’s next
research question and its associated hypotheses stated below.
RQ2: What are the relationships between FCBs, AMO factors and knowledge sharing in
the HKCI firms?
More specifically, these research questions attempt to observe the differences in the
impacts of FCBs with the factors in the AMO model (training for workers, motivation,
trust, and knowledge sharing). To answer this question, the following hypotheses were
developed.
H2.1: In the HKCI, FCBs act as a moderating factor in the relationship between
ability (training for workers) and knowledge sharing.
H2.2: In the HKCI, FCBs act as a moderating factor in the relationship between motivation
(incentive systems) and knowledge sharing.
H2.3: In the HKCI, FCBs act as a moderating factor in the relationship between opportunity
(trust) and knowledge sharing.
3.5 Conceptual framework of the research
Based on the above review and the study’s research questions, the following conceptual
framework has been developed for this study (See Figure 3.5 below).
57
Figure 3.5: Conceptual Framework
As such, five constructs (FCBs, training for workers (A), incentive systems (M), trust (O),
and knowledge sharing) are included in the above model to allow for an analysis of the
hypothesized relationships and estimating the direct and indirect effects.
3.5.1 Dependent variable
Consistent with model of knowledge sharing proposed by Zahra et al. (2006), similar
analytical procedures were applied to assess the effectiveness of the
implementation of knowledge sharing in FCBs. The two perspectives regarding how
knowledge is collected, stored and shared within a firm is considered, through the
formal and informal aspects of knowledge sharing (Table 3.5.1 for details).
As noted earlier in Chapter 2, formal (and explicit) knowledge is easier to collect and
transfer (Alavi et al., 2005, 2006: Leonard–Barton, 1995), whereas informal (and tacit)
58
knowledge is difficult to share (Lave, 1993; Lave and Wenger, 1991; Nonaka and Konno,
1998). Thus, it is noteworthy that “knowledge-sharing practices that focus on ‘communities
of practice’ that nurture and preserve the collective knowledge” tends to expedite direct and
informal knowledge (Heo and Yoo, 2002, p.3). Some studies also support the notion that tacit
knowledge is unstructured and shared by individuals through informal knowledge exchange
practices such as among peers or small groups (Lave, 1993; Lave and Wenger, 1991; Nonaka
and Konno, 1998; Nidumolu et al., 2001; Orlikowski, 2002; Tsai, 2002).
An FCB’s strong sense of identity, unique social system, and “familiness” (Habberson et al,
2003; Denison et al., Denison et al, 2004) can foster frequent informal discussions that, in
turn, can expedite the transfer of experience and knowledge among employees (Miller and
Le Breton-Miller, 2006). These complementary practices are very important, and the
practice of knowledge sharing are closely related to the constructs of training for workers,
incentive systems, and trust (Wong & Aspinwall, 2005; Mooradian, 2006; Zahra, 2007).
Thus, the factors affecting formal and informal knowledge sharing were explored via
empirical analysis in this research.
The effects of formal and informal knowledge sharing on the effectiveness of such an
activity was also examined following the method used by Zahra (2007). These questions
were examined and deemed appropriate for use (see Appendix I).
A number of dimensions of formal and informal sharing of knowledge is analysed for
assessing the overall frequency and nature of knowledge sharing activities in HKCI firms.
59
Table 3.5.1. Measuring Formal and Informal Knowledge Sharing (DV)
Formal knowledge sharing
1. How often do you use formal communication channels to share information with your
employees about "emerging technologies”?
2. How often do you use formal communication channels to share information with your
employees about "technological technologies”?
3. How often do you use formal communication channels to share information with your
employees about "changes in industrial conditions”?
4. How often do you use formal communication channels to share information with your
employees about "changes in customer needs”?
5. How often do you use formal communication channels to share information with your
employees about "changes in the strategies and tactics of your competitors”?
Informal knowledge sharing
1. How often do you use informal communication channels to share information with
your employees about "emerging technologies’?
2. How often do you use informal communication channels to share information with
your employees about "technological technologies”?
3. How often do you use informal communication channels to share information with
your employees about "changes in industrial conditions”?
4. How often do you use informal communication channels to share information with
your employees about "changes in customer needs”?
5. How often do you use informal communication channels to share information with
your employees about "changes in the strategies and tactics of your competitors”?
(Source: Zahra et al., 2006)
60
3.5.2. Independent variables
Three key independent variables (training for workers, incentive systems, and trust) were
examined here that have shown to play a crucial role in the extant literature in relation to
the level of knowledge sharing in firms. In order to measure these three variables (Cascio,
1986; Bollinger and Smith, 2001; Batt, 2002). this research adopted the measures
employed by Wong and Aspinwall (2005) to investigate the impact of incentive systems
and training for workers on knowledge sharing in a firm. The other independent variable
of trust, was studied using measures employed by Mooradian (2006) for investigating its
impact on knowledge sharing and its implementation in the HKCI. Tables 3.5.2a, b, c
respectively shows the specific items used for each independent variables of training for
workers, incentive systems, and trust. As noted earlier in the review of KM literature, KM
involves the key processes of knowledge sharing and integration. To this end, keeping in
mind the study’s focus on knowledge sharing, this research measures the nature and
extent to which firms in the HKCI engages its employees in activities that supports the
concepts of knowledge and knowledge management through the (1) provision of training
for its workers; (2) provision of incentives and rewards for its workers to acquire and share
knowledge within firms; and (3) fosters an environment of trust among employees’ peers
and managers for creating effective sharing of knowledge in the HKCI.
61
Table 3.5.2a: Training for Workers
1. Training on the concepts of knowledge and knowledge management (KM)
2. Building awareness of KM among employees through training.
3. Training on using the KM system and tools.
4. Training for individuals to take up knowledge-related roles.
5. Training in skills development, such as creative thinking, problem solving, communication, soft networking, team building, etc.
(Source: Wong and Aspinwall, 2005)
Table 3.5.2b: Incentive Systems
1. Providing the right incentives to encourage KM behavior.
2. Motivating employees to seek knowledge.
3. Visibly rewarding employees who share and use knowledge.
4. Rewarding employees with an emphasis on group performance.
5. Creating motivational approaches to job performance assessment system.
(Source: Mooradian, 2006)
Table 3.5.2c: Trust
Trust in peers
1. If I got into difficulties at work, I know my colleagues would try and help me out.
2. I can trust the people I work with to lend me a hand if I needed.
3. Most of my colleagues can be relied upon to do as they say they will do.
Trust in management
1. Management at my firm is sincere in its attempts to meet the employees’ point of view.
2. I feel quite confident that the firm will always try to treat me fairly.
3. Our management would be quite prepared to gain advantage by deceiving the employees (reverse coded).
(Source: Mooradian, 2006)
62
3.5.3. Moderator
A moderating variable’s effect can assess the strength of a relationship or alter the direction
between an independent and dependent variable (Baron and Kenny, 1986; Holmbeck,
1997; James and Brett, 1984). By a further extension of these concepts, a moderator can be
viewed as a variable that provides an interaction whereby the effect of one variable
depends on the level of another (Frazier et al., 2004). Therefore, in this study, FCBs are
considered as a moderator and the ways in which the moderator effects the relationship
between the IV and DV can be further analyzed. The measurement of whether a firm is an
FCB or a Non-FCB is measured based on questions from Chua et al. (2004), as shown in
Table 3.5.3 below.
Table 3.5.3.: Identification of FCBs
1. The percentage of family ownership of business.
2. The percentage of family members being managers in the business.
(Source: Chua et al., 2004)
3.5.4 Additional background data
Aside from independent and dependent variables, demographic information, though not
analysed in-depth, does provide a better understanding of the responding firms’
characteristics. This is done to access various elements and avoid missing any useful
information during the analysis. The summary of demographic information is show in Table
3.5.4. Following Chu et al. (2004), this study’s research questions were developed to
identify whether the participating firm is an FCB or a Non-FCB. Participants were asked
questions about the percentage of FCB equity and the number of family members in top
63
management, which are crucial elements in evaluating the impact of AMO factors. Both the
number of generations to have succeeded in sustained family management and years of
operation may also have an impact on the firms’ overall performance. Furthermore, firm
size in terms of number of employees, along with a firm’s industry sector have also been
found to have an impact on its business performance (Harter et al., 2002; Calof, 1994). The
empirical research by Wong and Aspinwall (2005) was considered appropriate for the
measurement of the independent variable of ability (training for workers) and motivation
(incentive systems) in the HKCI. To examine the impact of training for workers, the study
employs existing instruments with theoretical concepts to enhance the validity of
constructs. The same study (Wong and Aspinwall, 2005) was used as a reference in
investigating whether incentive systems facilitate the knowledge-sharing process.
The questions focusing on FCBs factors and demographic information that are included
in this questionnaire uses the approach of uneven interval scale measurement. Wong and
Aspinwall (2005), Yang (2007), and Hoare (2006) suggested that the measurement
intervals does not need to represent an equal scale in the administration of survey and
measurement of variables. Therefore, some of the survey questions are not based on an
even measurement interval scale. In collecting some demographic data on enterprise size,
this study employs the following class intervals. For example, the number of employees in a
small-sized firm are considered to be those employing less than 25 staff; medium-sized firm
are considered to be those employing between 26 to 200 staff; for firms are considered to be
those employing 201 to 3,000 staff; and state-owned enterprises (SOEs), are considered to
be those employing over 3,000 staff.
Similarly, regarding increases in labour productivity, the measurement intervals for this
factor focuses on different percentages of family member proportions and it range from
between “< 10%” to “> 80%.” Other such factors where class intervals are uneven include:
64
percentage of equity owned by family members, years of operation of an enterprise,
average sales and its increases in the last three years.
65
Table 3.5.4.: Demographic Questions in the Questionnaire
1
Family-controlled businesses (FCBs)
I) FCBs, ii) Non-FCBs
2
Numbers of generation have succeeded–family management
i) 1 ii) 2 iii) 3 iv) 4 v) 5
3
Percentage of family members
i) < 10% ii) > 10%–30% iii) > 30%–50% iv) > 50%–80% v) > 80%
4
Equity of your enterprise owned by a family
i) < 5% ii) > 5%–15% iii) > 15%–30% iv) > 30%–50% v) > 50%
5
Position in the enterprise
i) CEO ii) General Manager iii) Managing Director iv) COO v) Others
6
Years enterprise has been operating
i) < 1 year ii) > 1–5 years iii) > 5–10 years iv) > 10–15 years v) >15 years
7
Number of employees
i) < 50 ii) 51–200 iii) 201–1,000 iv) 1,001–3,000 v) > 3,000
8
Category of your enterprise in the HKCI
i) Service (material supply) ii) Manufacturing iii) Product (own brand) iv) Component v ) Product Trading
9
Average sales per year in the past three years (RMB)
i) < 1 million ii) > 1–10 million iii) > 10–50 Million iv) > 50–100 million v) > 100 million
10
Increase of sales in the past three years
i) < 10% ii) > 10%–40% iii) > 40%–70% iv) > 70%–100% v) > 100%
11
Increase of labor productivity (the revenue contributed by an on-duty worker) in the last three years
i) < 10% ii) > 10%–40% iii) > 40%–70% iv) > 70%–100% v) > 100%
66
3.6 Questionnaire design and sampling
This section discusses the process of developing the questionnaire for this thesis. As shown
in Appendix 3.1and Table 3.4, this research adopted items already used and tested in the
extant literature (Chua et al., 2004; Mooradian, 2006; Wong and Aspinwall, 2005; Zahra et
al., 2006).
3.6.1. Measurement and scales
Anderson and Gerbing (1988) suggest that researchers should first estimate a measurement
model before testing their hypotheses to avoid any misinterpretation of structural
relationships. The measurement and scales were qualified and selected based on the
relatively high values of validity and reliability for all the variables (Baker, 2002a). Thus, all
questions were highly relevant to the hypotheses being tested. A seven-point Likert scale
was developed for the measurement of the data collected from the participants(Sekaran
and Bougie, 2003). The executives and managers of firms in the HKCI were targeted to be
part of the research sample. The next step was to determine the sampling frame following
a set of directions (Malhotra 2008). The survey questionnaire was organized into three
sections: 1) questions about the independent variables (IV) consisting of incentive systems,
training for workers, trust, and FCBs; 2) questions about the dependent variable (DV), such
as effectives of knowledge sharing in the HKCI firms; and 3) questions pertaining to
demographic data, such as age, size, and nature of business.
67
3.6.2 Data collection and sampling
Data can be collected in several ways, such as face-to-face or personal interviews,
personally administered forms, mailed or electronic surveys, and telephone interviews.
Each method has its own set of advantages and disadvantages (Dillman et al., 2009; Fowler,
1988; Frazer and Lawley, 2000; Malhotra, 2008; Sekaran, 2003; Zikmund, 2003). This
research had a large sample size and used an Internet-based questionnaire for collecting
data from employees of firms in the HKCI =. The data was collected by administering a
survey through an email invitation to firms in the HKCI using trade directories. Given the
geographically dispersed distribution of firms in Hong Kong, the choice of sending
questionnaires by email to 900 HKCI firms was considered as an appropriate strategy. Details
of the sampling, electronic survey and its administration are discussed next. The steps
involved in the selection of the research population, sampling frame, size, selection and
administration of the survey is shown in Figure 3.6.2
Figure 3.6.2 Steps in Sample Determination and Survey Administration
68
3.6.3. Defining the Research population
To achieve the purpose of the current research, data on both FCB and Non-FCB firms were
distinguished to ensure a sufficient number of participants are received for both the groups.
In relation to the above, the research by Klein, Astrachan, and Smyrnios (2005) emphasized
three dimensions of family business: culture, power, and experience. These ideas are
consistent with the extant literature that focuses extensively on family culture.
Identifying and defining the subject may be difficult, but for the purposes of this research,
we employ the definition by Martos (2005, p. 167) who defined an FCB as a “a firm in which
the members of a single family have a sufficient stockholding to dominate the decision
taken by the owners’ representative body, whether this has a formal and legal character or
in the contrast is informal, and in which moreover there is desire or intention to maintain
the business in the hands of the following family generation.” Targeting the executives and
managers as the participants of this research aimed to clarify the concept of FCBs and
conduct an objective assessment of the different variables being examined.
Firms belonging to the HKCI comprised the sample population of this research. They must
have offices in Hong Kong and businesses registered under the HSIC code (Hong Kong
Standard Industrial Classification Code).
The Participant Information Sheet (See Appendix II) was emailed along with the other
attachments (consent form) to all potential participants in the HKCI. The survey questions
were approved by the Human Ethics Committee of the University of Newcastle, Australia
and the participants’ confidentiality were protected as per the Human Ethics guidelines and
standards of the University.
69
3.6.4. Selection of sample
A simple convenient sampling approach was employed in the selection of sample for this
research. An invitation to take part in the online survey was sent by e-mail to employees
from a total of 900 firms in HKCI sector through convenience sampling. The email addresses
and telephone numbers were selected from the yellow pages database of Hong Kong
business directories. The HKCI-related businesses, such as services (material supply),
manufacturing, products (owned brand), and product trading companies, were chosen for
this research. A convenience sampling (FCBs and Non-FCBs) approach was applied. Surveys
are easily accessible (Fraenkei et al., 1993), particularly online surveys, even when the
desired respondents are hard to access (Fricker and Schonlau, 2002). However, some have
argued that this approach may diminish the external validity (Mann, 2003). Assuming a
conservative response rate of about 20.5% from the 900 questionnaires emailed, this
method was expected to yield a little under 180 usable and returned questionnaires. SPSS
software version 22 was utilized for performing reliability, validity, multiple regression, and
correction analyses. To avoid any sample errors, Cronbach’s alpha was used to assess the
reliability and confirmatory factory analysis (CFA) was used to examine the validity of all
independent and dependent variables before hypothesis testing (Baumgartner and
Hombury, 1996; Bollen, 1989).
3.6.5. Sampling frame
A sample frame consists of a list of elements or direction from which a representative
sample may be drawn for a study (Fowler, 1988; Malhotra, 2008; Saharan, 2003; Sigmund,
2003). In the current study, clothing manufacturing firms in Hong Kong were chosen as the
sample (Conway, 1997; Rouette, Fischer-Bobbie and Carl-Heinz, 2001). The study followed
Hong Kong Industry Department’s (2000) definition of a Hong Kong manufacturing firm,
70
which is an organization that transforms raw material (fabric) by machine or by hand into
products (garments). Such a firm handle either part or all of its manufacturing processes
locally (in Hong Kong). Such an organization must at least have a headquarter or an office
in Hong Kong, for coordinating its manufacturing operations in China and other countries
for it to be regarded as a manufacturing firm and is thus included in the research.
3.6.6. Sample size
Reliable information on family firms is difficult to collect (Handler, 1989). The sample
frame for this research consisted of managers or senior executives and owners from 900
firms in the HKCI. Khamis and Kepler (2010), used a reliability criterion and proposed a
minimum sample size of ‘n’, which is equal or greater than 20+5k (where k refers to the
item number of independent variables). In this research, we finalized a total of 26 items in
the questionnaire to target a sample size of a bit over 100. According to Alreck and Settle
(2003), when the population is large, experienced researchers would consider 100
respondents to be the minimum sample size so that the minimum number of response rate
can be achieved.
This study employed a big sample size to enhance the likelihood of undertaking
substantive statistical analysis. Wilson Van Voorhis and Morgan (2007) note that a
population of 900 participants is a minimum level that can be considered acceptable for
a research of this nature. Furthermore, this study employed use Process Macro in SPSS to
bootstrap the size to 1000. This procedure shall be discussed in a subsequent section.
71
3.7. Data collection method
All the 900 firms listed in the directory were extracted from the yellow pages. One person
from each company listed in the directory was contacted via an email. A single respondent
approach has the advantage of maintaining confidentiality and giving the respondent a
feeling of identity protection and risk reduction so that she or he can respond candidly
(Kohli, 1898; Lai et al., 2007).
As mentioned earlier, obtaining reliable information on family firms (Handler, 1989) is
difficult. However, by employing a big sample size and deploying an electronic survey
instrument to collect the data, from HKCI firms, this study was able to collect 100 plus
responses.
3.7.1. Administration of data collection
An electronic survey questionnaire allows respondents to complete the questionnaire at
their most convenient time and place (Sekaran, 2003). Given that this research required a
large number of participants from firms in the HKCI, this study assumed that (1) most
participants can answer the questionnaire upon receipt of the invitational email and
following the consent to participate in the study; and (2) they would be able to complete it
during office hours as they would have access to the Internet and a desktop or a similar
device at the time. The data collected through this survey protects the respondent’s
confidentiality and such an approach helps obtain sensitive financial or personal
information as well as increase response rates (Fowler, 1988; Lockhart and Russo, 1994:
Mathotra, 2004; Zikmund, 2003). Another advantage of this approach is that it is low in cost
(Sekaran, 2003; Shao, 2002; Zikmund, 2003) and requires no special training for
respondents to complete the surveys (Sekaran, 2003).
72
A Participant Information Sheet outlining the details of the project was distributed to each
firm via email. The senior managers were invited to participate in the survey by clicking on
the web link, voluntarily, to complete and an anonymous questionnaire. The participant
information sheet contained contact and details about the project.
3.7.2. Data analysis
In order to test whether the research results supported the study’s hypotheses, several
statistical tests, such as CFA, Pearson’s product moment correlation, and multiple
regression analysis, as well as descriptive analyses, were employed to analyse the
information collected through the online questionnaire.
3.8. Power of tests of interactions
The answers from respondents were first analysed descriptively using frequency
distribution and mean scores to test for the statistical significance of the respondents
between the two categories of enterprise type (FCBs and Non-FCBs). The process of
probing interactions whenever a moderation model is specified with X ‘s (independent
variable) effect on Y (dependent variable) is moderated by another variable. A moderator
is a variable that moderates the direction or strength of the correlation between
independent and dependent variables (Baron and Kenny, 1986; James and Brett, 1984). A
moderator’s effect is considered as an interaction that explains the effect of how one
variable depends on the level of another variable (Frazier et al., 2004). In this study, the
moderating effect of FCBs can be examined though the above statistical tests.
In addition, Process Macro in SPSS was run with covariate approaches to analyse the
moderation effects of FCBs between AMO variables and knowledge sharing. Here,
73
bootstrapping was used to infer the original data (Hayes, 2013, Williams and
MacKinnon, 2008). This method requires running more than one regression so that the
main effects of each interaction can be examined. Implementing the Process Macro
automatically generates an output of Simple slope from the analysis (Aiken and West,
1991; Cohen et al., 2003; Hayes, 2005). This procedure involves selecting a value of the
moderator (M), and calculating the conditional effects of X on Y at the values of the
moderator, and conducting an inferential test or generating a confidence interval. To do
so, an estimate of the standard error of the conditional effect of X is required for the
selected values of M (Aiken and West, 1991; Cohen et al., 2003).
A three-step approach was used in assessing the moderation effect test. In the first step,
the tolerance of the variance inflation factor (VIF) of each variable in the model was
examined to identify any multicollinearity issues that may create problems for regression
analysis results (Hair et al., 1995). A high VIF indicates presence of multicollinearity
between the explanatory variables. A high VIF value means a higher inter-correlation
among the variables. A VIF that is higher than 10 means that the variables are likely to be
affected by multicollinearity subject to tolerance from .05 to 10 (Belsley et al., 1980;
O’Brien, 2007). Eigenvalues close to 0 indicates a dimension that explains little variance
(Collins, 2009). Given that the causal regime is structured into the data, it becomes
possible to assess the degree to which various modelling approaches produce accurate
coefficient estimates. One of the methods used is to bootstrap the sample size to increase
it from 100 to 1000 (Hayes, 2013); as with a larger sample size, the standard errors
becomes much smaller.
In the second step, Process Macro was run in SPSS with the covariate approach. As the
sample size was small (around 119), the bootstrap confidence intervals were based on the
same set of 1,000 resamples from data obtained via the Process Macro in SPSS multiple
74
analysis (York, 2012). This method is powerful and can be easily extended to be consistent
with the models using statistical control only. Process Macro can explain both the direct
and indirect effects in a moderation analysis as well as perform the inferential test for each
construct mathematically. At this point, by adding an interaction term to the model is
helpful if we want to test a hypothesis that focuses on correlations between AMO factors
and knowledge-sharing behaviours with FCBs as a moderator. A linear relationship should
exist between the dependent variable and the covariate for each group. Therefore, if there
is a case of high multicollinearity, such analyses can yield sufficiently reliable results with
which to test the study’s hypotheses (York, 2012).
In the third step, a simple slope analysis was generated to explain the regression analysis
for each moderator (interaction) results. Here, the selection of various values of M at which
to estimate the conditional effect of X on Y would be required. Discussing the interactions
and interpreting the results have been widely recommended (Aiken and West, 1991; Chen
et al., 2003). However, different choices are often made arbitrarily, which can be fixed by
following the John–Neyman Technique for moderator difference analysis, especially when
M is highly skewed (Hayes, 2013).
3.9. Descriptive statistics
The current research adopted a descriptive analysis to gather observations about the data
collected for analysis. Standard deviation is a measure of central tendency and variability,
including median, mode, mean, skewness, and kurtosis. The survey may have a structured
research design and an appropriate number of respondents to maximize reliability and
minimize errors (Barlett et al., 2001; Fowler, 2013).
Additional demographic data for the research were collected to obtain information about
75
number of employees, their position in the firm, and the equity percentage of family
members. The standard deviations and the mean values of each variable (FCBs, training for
workers, incentive systems, trust, and knowledge sharing) were analysed to examine the
level of normal distribution. Furthermore, the two groups of demographic data of FCBs and
Non-FCBs in HKCI were sorted to identify the differences between them.
Normality is a technique that is used to test whether the collected data can satisfy the
assumptions for subsequent statistical analysis that will be undertaken. For this, a
histogram was used to examine the information graphically and to explain the distribution
of each assessed item. Additionally, the values of skewness and kurtosis, which identifies
the shape of distribution, were also used to examine the normality. Homogeneity of
variance was tested for all variables to ensure that the assumption of normality was
satisfied. Both normality tests were used to obtain supporting data for this investigation.
3.9.1. Reliability and validity
Ethical issues may be considered at all stages of the research design process. The
researchers may ensure that the questionnaire items can consistently and accurately
assess what they are supposed to assess and do so in a reliable and valid manner (Sigmund,
2003). All efforts were made to comply with the requirements set by the Human Ethics
Committee of the University of Newcastle, Australia. In line with the Ethics approval, the
researcher will not disclose the participants’ details to other parties and ensure the
confidentiality of information provided by the respondents (Collis & Hussey, 2013; Corti &
Backhouse, 2002; Mackey & Gass, 2015). Furthermore, the participants were informed
that the data collected would not be used for any commercial purposes. Finally, the
76
researcher’s non-involvement with the HKCI is declared; hence, there are no conflict of
interests.
The reliability of a measure refers to the extent to which the assessments are free from
error, and that the results would remain consistent even if repeated assessments are
adopted (Malhotra, 2008; Sekaran, 2003; Zikmund, 2003). This study replicated questions
covered in the extant literature (Chua et al., 2004; Mooradian, 2006; Wong and Aspinwall,
2005; Zahra et al., 2006). A detailed assessment of reliability and validity tests using SPSS,
is presented in later sections.
Reliability also refers to the proportion of variability in a measured mark, which is identify
the variability in the true marks, rather than the type of errors. A reliability of .90 means
90% of variability in the observed marks are true and 10% is from error. Some limitations
in measurements such as test–retest reliability is potentially improper if a respondent’s
prior experience in the first testing affects his/her responses in the second testing
(Carmines & Zeller, 1979).
Whereas validity explains the extent to which a measure precisely preforms the concept
(Punch, 1998). Two broad measures of validity include: internal and external and validity.
Internal validity states the reasons for the findings of the study, and helps reduce
unanticipated reasons for these findings. In comparison, External validity shows the ability
to employ with confidence the results of the study to other people and other situations as
well as ensure that the “conditions under which the study is carried out are representative
of the situation and time to which the results are to apply” (Black, 1999, p. X).
Using established methods to access the validity and reliability of the research is one of
method to produce trustworthy and useful findings. In deciding the reliability and validity
77
of a research, diminishing error is an essential factor. In any case, there is no perfect set of
procedure without limit error. All measures bring some residual bias, inaccuracy or
unreliability (Punch 1998). While endeavours can be applied to diminish such risks or
especially systematic errors, they are perceived as a limitation in all type of research.
Although researchers may employ as many methods as possible to secure validity and
reliability; still, there remains the likelihood that flaws may happen at the outline,
measurement, or analysis stage, ultimately finding in under ideal research findings.
3.9.2. Reliability analysis with Cronbach’s alpha test
Cronbach’s alpha test is a common statistical approach to measure internal reliability (Shin
et al., 2000). A Cronbach’s alpha of above .70 is considered satisfactory (Nunnally, 1978),
which means that the measurement is reliable and suitable for similar studies in different
environments (Gold et al., 2001; Chuang, 2004), such as Hong Kong.
3.9.3. Testing the moderating effect
The moderator (FCBs) effect was assessed for H2.1, H2.2, and H2.3 on training for workers,
incentive systems, and trust were assessed.
The interaction variables were included in the multiple regression, and the R ²
(coefficient of determination) of those regression models assessed showed some
change in the statistics. Considering statistical significance and practicality, the p-
value of the interaction variables of less than .05 indicates that the relationship is
significant.
78
H2.1 proposes that the impact of ability on knowledge sharing is moderated by FCBs.
Referring to Saharan & Bougie (2010), the moderator effect is illustrated by the interaction
of training for workers and FCBs in explaining knowledge sharing. Multiplying the measured
item creates the product variable, and for the scores of ability (Training for workers) and
FCBs, the interaction variable (termed as ATW x AQ) is used to determine if this moderator
influences knowledge sharing. The Multiple regression model is illustrated in Figure 3.9.3a.
Figure 3.9.3a Regression Model for Ability (Training for Workers) and FCBs
Using the multiple regression model for assessing H2.1 shown above, the effect of ability
(training for workers) on knowledge sharing and the moderating effect of FCBs were
assessed using Sharma et al. (1981). The presence of a significant interaction demonstrates
that the impact of one predictor variable on the response variable is distinctive at various
values of other predictor variables. This assumption is examined by adding an interaction
term to the model, in which the two predictor variables are multiplied. Adding an
interaction term to a model changes the interpretation of all the coefficients; thus, it is more
valuable to comprehend the moderating effects (Hair, 2016; Hayes, 2013).
79
Similar to the above analysis, multiple regression was used to test H2.2. This moderation
test illustrates that the effect of motivation (incentive systems) on knowledge sharing is
moderated by FCBs. Multiplying the measured item creates the product variable, and for
the scores of motivation (Incentive systems) and FCBs, the interaction variable (termed as
AIS x AQ) is used to determine if this moderator influences knowledge sharing. The
mult ip le regression model is shown in Figure 3.9.3b, and the moderator effect is
developed as stated below.
Figure 3.9.3b: Regression Model for Motivation (Incentive Systems) and FCBs
Similarly, H2.3 proposes that FCBs positively act as a moderating factor in the relationship
in between trust and knowledge sharing, as shown in Figure 3.9.3c.
80
Figure 3.9.3c: Regression Model for Opportunity (Trust) and FCBs
Similar to H2.3, the moderating effect is examined via the multiple regression analysis.
Multiplying the measured item creates the product variable, and for the scores of
opportunity (trust) and FCBs, the interaction variable (termed as AT x AQ) is used to
determine if this moderator influences knowledge sharing.
Figure 3.9.3c indicates the summary of the regression analysis data for testing the
moderator effect between opportunity (trust) and knowledge sharing.
3.10 Summary and limitations
This chapter outlined and a summary of the testing methods of all the hypotheses is
provided in Figure 3.10 below.
81
Figure 3.10: Flow Chart of the Measurement Methods Used in the Research
Further, the chapter provided a systematic review of the methodological steps employed in
this thesis: progressing from descriptive statistics through assessing the reliability and
validity of constructs to testing the study’s hypotheses. The chapter also highlighted the
strengths and weaknesses of each approach. The next chapter discusses the results, analysis
and testing of the hypotheses.
82
Chapter 4
Data Analysis and Results
4.1 Introduction
The previous chapter described the methodological approach adopted to collect the data
for this research. This chapter presents the analysis of the survey data and the findings for
each hypothesis are examined. A total of 119 valid responses were returned from the 900
targeted participants of a survey of Hong Kong clothing industry (HKCI) firms between
early February to mid-April 2016, representing a 13.2% response rate. Using SPSS (ver.
22), the data was analysed. This chapter is organized into four parts, a summary of the key
hypotheses being tested through the data analysis is shown in Figure 4.1.
Figure 4.1: Conceptual Model for AMO model in Knowledge sharing
83
The rest of the chapter is structured as follows. Part 1 introduces the chapter. Part 2
presents how the data was collected and analysed. Part 3 deals with the characteristics of
the independent and dependent variables. Part 4 presents the descriptive statistics and
reports on the measurements of the constructs to facilitate further analysis. Part 5
presents the testing of reliability and validity of the data collected. Part 6, presents the
analytical approaches for testing the study’s six hypotheses. These are discussed in the
order of their presentation. Finally, a summary of the results is presented.
4.1.1 Data preparation
Data preparation involves data coding and cleaning the data prior to analysis; such a
process ensures the accuracy of data from its crude form (Malhotra, 2012). The
preparation of a crude structure into an appropriate data structure is crucial, because it
makes the data usable for subsequent computerized statistical analysis (Fowler, 1988;
Banerjee et al., 2014). To ensure the quality and convergence, the data were categorized
into different classes (Brown et al., 2014). Responses from participants were collected,
sorted, coded, cleaned, screened, and classified into different categories (Fowler, 1998;
Malhotra, 2004).
Data was analysed to check the precision of the information and to get a general picture
of the phenomena under study (Hair et al., 2006; Malhotra 2007; Sekaran, 2003).
Particularly, primary analysis comprised of an assessment of the effects of missing data,
non-response error, identification of exceptions and dispersion normality (Hair et al.,
2006; Malhotra 2007). The percentage of missing data for each item was noted. A case is
completely barred from all the analyses if it is missing even a bit of data, because it can
seriously, constrain the sample size (Pallant, 2001; Hair et al., 2010). If there are only ≤5%
84
missing values in a random example, the problem is less critical, and most systems
handling missing value yield comparable results (Tabachnick et al., 2001). Overall, the aim
was to ensure the propriety of information for further statistical tests to be run using SPSS
(ver. 22).
4.1.2. Data coding and entry
After the questionnaires were checked, numbers were assigned to represent the
participants for each question on the questionnaire, thus facilitating data coding
(Malhotra 2004; Zikmund, 2003). Most of the responses on the questionnaire were
precoded to make them less complex and to help identify the items adopted for empirical
analysis. Next, the coded data were entered into SPSS (ver. 22) (Sekaran, 2003; Hair et al.,
2006). Knowledge sharing (KS) include formal and informal knowledge and Table 4.1.2
shows the classified codes for all the variables used in this research.
85
Table 4.1.2 Data Coding for all Measurement Variables
Key Variables Measurement Items Codes Remarks
Position in firm DPIF
Number of employees DNOE
Years of firm in operation DYFO
Business category in the HK clothing industry? DBC
Average sales per year in the past 3 years ( HK$) DAS
The increase of sales in the past 3 years DIOS
The increase of labour productivity ( the revenue
contributed by an on-duty worker ) in the last 3
years. DILP
Family control businesses FCBs
Equity of your firm owned by family FQF
Precentage of family members FPM
Numbers of generation have succeeded to family
management FNG
Ability Training for workers TW
All training of
workers factors are
prefixed with letter"
IS". TW_1,2,3,4,5
Moivation Incentive systems IS IS_1,2,3,4,5
Tust Tust T T_1,2,3,4,5,6
Formal knowledge FK K_1,2,3,4,5
Informal knowledge IK K_6,7,8,9,10
Family
Demographic
All individual
demographic
variables are prefixed
with letter " Q"
All family related
variables are prefixed
with Leter " Q"
Knowledge sharing
86
4.2. Sample profile
As indicated in Chapter 3, the questionnaires were sent out to 900 firms located in the
HKCI. A total of 120 responses were received, yielding an overall response rate of 13.3%.
A total of 119 usable responses were included in the analysis. The demographic data were
gathered in the first section of the questionnaire, with the data of different variables
included in subsequent sections.
First, the demographic information was analysed, and the descriptive statistics were
generated to perform an analysis of any significant factors. The response frequency and
descriptive statistics of those factors are presented in Table 4.2a
Respondent’s position in the firm
As shown in Table 4.2a, most of the respondents (96.6%, 117 responses) occupied senior
positions at their respective organizations. Accordingly, given the high level of senior
management represented in the sample, it is noted that these employees were
knowledgeable about the business and operation of the enterprise, thereby implying that
are capable of completing the survey.
Years of operation
Referring to Table 4.4.2, majority of the responding firms (59 %, 63 responses) had been
established for 10 years or more. Overall, only 22.7 % (27 responses) of the responding
firms have been in existence for less than a year. This demonstrates that most
respondents have had long-term business involvement in the HKCI.
87
Firm size and employment
For the purposes of this study, the number of employees in a firm is used as a proxy for
the size of a firm and it includes both factory workers and office staff. Marketing,
merchandising, managerial, and financing employees were grouped as office staff (Moon,
2001). All organizations who participated in this study had an office in Hong Kong. As
noted in Table 4.4.2, the number of workers in HKCI ranged from <50 to >3000 employees.
As per the Hong Kong Trade and Industry Department (2000), small and medium-sized
enterprise (SMEs) are characterized as those manufacturing firms that employ fewer than
100 HK workers whereas non-manufacturing firms that employ fewer than 50 employees
are considered small and medium-sized firms. The results indicate that the HKCI has a lot
of small and medium-sized manufacturing and non-manufacturing firms. It is also
indicated by the government’s statistics that most of the firms (>98%) that are engaged in
the service and manufacturing sectors in HKCI are SMEs (Hong Kong Trade and Industry
Department, 2000). In this study, 50.4% (60 firms) of the sample firms had local workers of
fewer than 100 individuals; thus, a bit over half of the participating firms in the sample
belonged to the SME group (Table 4.2.2).
Business category of the firm in the HKCI
The categories of a firm identified within the HKCI, which were covered in this study
included the following: manufacturing, product (own brand), product trading and product
services (material suppliers). A total of 39 responses were from manufacturing (32.8%), 30
respondents (25.2%) were from product (own brand) firms and 28 (23.5% respondents)
were from product trading firms. The remaining 16% (19 respondents) were from product
services (material supply) firms from the HKCI.
Performance Measures
88
Annual sales in the past three years
To measure the business performance of HKCI firms, each respondent in this study was
asked to assess his or her company’s present business performance in terms of sales
growth, productivity, and annual sales in the previous three years.
Of the 119 responding firms, the majority (55.5%, 66 responses) of respondents reported
that their companies had less than US$10 million in average annual sales turnover in the
past three years, whereas respondents from 17 organisations (14.3%) reported a sales
turnover between US$50 and US$100 million (See Table 4.4.2 for details).
Increase of sales in the past three years
A little over two-thirds of the respondents (68.1%, 81 responses) reported less than 10%
increase in sales over the past three years, and only 2.5% (3 responses) reported sales
increase of over 70%–100% (Table 4.4.2).
Increase of labour productivity in the past three years
Finally, most of the respondents (73.1%, 87 responses) had less than 10% increase in
labour productivity (i.e., the revenue contributed by an on-duty employee) in the past
three years, and only a small percentage (22.7%, 27 responses) reported productivity
increases of between >10% and <=40%, only three companies (2.5%) reported
productivity increases of between 40%–70 %, and two firms (1.7%) reported the highest
increase in productivity of between 70%–100% (Table 4.2a).
For the purposes of this study, the respondents and their corresponding firms were
deemed appropriate representatives for this sample as they had been in operation for a
long time. Each of the 119 responders were HKCI sector firms. Additionally, given that
89
most Hong Kong managers could communicate in English, there was no apparent issue
discovered concerning the capacity of the respondents to comprehend the definition of
any specific word or phrase in the questionnaire. The other demographic profiles and
characteristics of the respondents and employees, such as year of operation, are
presented in Table 4.2a.
Table 4.2a Response Frequencies of Demographic Data
Response Percent Response Count
1.CEO 11.8 14
2. GENERAL MANAGER 0.8 1
3. MANAGEING DIRECTO R 16.8 20
4. CO O 16.8 20
5. O thers 52.1 62
1. =or <1 22.7 27
2. >1 to 5 17.6 21
3. >5 to 10 11.8 14
4. >10 to 15 41.2 49
5. >15 6.7 8
1. =or <50 50.4 60
2. 51-200 15.1 18
3. 201-1,000 5.9 7
4. 1,001-3,000 16 19
5. > 3,000 11.8 14
1. Manufacturing 32.8 39
2. Product (owned brand) 25.2 30
3. Product Trading 23.5 28
4. Service (material supply) 16 19
1. = or < 1 31.1 37
2. >1 to 10 24.4 29
3. >10 to 50 10.1 12
4. > 50 to 100 20.2 24
5. > 100 14.3 17
1. = or < 10% 68.1 81
2. >10% to 40% 26.1 31
3. >40% to 70% 3.4 4
4. >70% -100% 2.5 3
5. > 100% 0 0
1. = or < 10% 73.1 87
2. >10% to 40% 22.7 27
3. >40% to 70% 2.5 3
4. >70% -100% 1.7 2
5. > 100% 0 0
Position in your enterprise
The% increase of labour productivity ( the revenue contributed by an on-duty workers ) in the past 3 years.
The % increase of sales in the past 3 years
Average sales per year in the past 3 years (HK$ million(s))?
Number of employees?
Number of year(s) your enterprise has been operating?
Business Category that your firm belongs to the clothing industry
90
4.3 Characteristics of dependent and independent variables
4.3.1 Profile of FCBs and Non-FCBs
In this study, FCBs and Non-FCBs are thought to influence the relationships between the three
AMO factors [motivation (incentive systems), ability (training for workers), and opportunity
(trust) factors] and knowledge-sharing outcomes. Referring to the classification in Table 4.3.1a,
56.3% of the respondents (67 out of 119) were classified as FCBs. Further, nearly 60.5% of the
respondents (72 out of 119) stated that less than 10% of family members were in top
management, and 49.6% (59 out of 119) family members had at least 5% stake in the equity
capital of the enterprises. Finally, 52.9% of the respondents noted that family-owned companies
(63 out of 119) had succeeded for one generation.
Table 4.3.1a Response Frequency of FCBs Data
Measuring items( Family business related) Response Precent Response Count
Do you think your organization is a family enterprise?
Yes 56.3 67
No 43.7 52
Percentage of family members in the top management team?
less than or =10% 60.5 72
>10 to 30% 13.4 16
>30% to 50% 7.6 9
>50% to 80% 6.7 8
more than 80% 10.9 13
Equity of your enterprise owned by family members?
less than or =5% 49.6 59
>5% to 15% 7.6 9
>15% to 30% 31.9 38
>30% to 50% 6.7 8
more than 50% 4.2 5
How many generation(s) have succeeded to family management?
1 52.9 63
2 32.8 39
3 9.2 11
4 0.8 1
91
4.3.2 Descriptive statistics of items in this study
A seven-point Likert scale for the online survey questionnaire (1=Strongly disagree; 2=Disagree;
3=Moderately disagree; 4=Neither agree nor disagree; 5=Moderately agree; 6=Agree;
7=Strongly agree) for measuring the characteristics of all variables. These are shown in Table
appendix E.1- E.4.
Ability (Training for Workers)
Refer to Appendix E.1, the mean and standard deviation are used to check the ratio and scale of
interval measures. The mean and standard deviation values were utilized to quantify the
variation in a set of data as well as highlight the value of the data set that is different from the
mean (Zikmund et al., 2003). The mean results of training for workers varied from 4.26 to 4.59
and has a standard deviation of 1.44 to 1.64 between the highest and lowest scores. The overall
of the mean and standard deviation of this construct are 4.41 and 1.38 respectively. The
outcomes suggest that numerous respondents embraced training of employees to improve their
performance to effectively share knowledge and understand KM concepts in their respective
companies.
Motivation (Incentive Systems)
Refer to Appendix E.2, the mean of incentive systems measurement items ranged from 4.48–
4.67, with a standard deviation of 1.39 to 1.49 between the highest and lowest scores. Then,
the overall of the mean and standard deviation of this construct are 4.56 and 1.31 respectively.
The values suggest that on average, many respondents viewed incentive and reward policies to
motivate their work performance. To ensure the effective implementation of knowledge
management, incentive systems are critical in family enterprises (Chrisman et al., 2007). Wong
92
and Aspin Wall (2005) have reported that incentive systems provide motivation to drive workers
to engage in knowledge sharing.
Opportunity (Trust)
Refer to Appendix E.3, the mean of Opportunity (Trust) measurement items varied from 4.74–
5.08 with a standard deviation of 1.26-1.4 between the highest and lowest scores. Then, the
overall of the mean and standard deviation of this construct are 4.85 and 1.19 respectively. The
results support the notion that trust among employees is strongly relied upon by many
respondents to share knowledge.
Knowledge Sharing
Refer to Appendix E.4, this study’s dependent variable of knowledge sharing (formal and
informal) has items in the questionnaire that further delineates this dependent variable. The
information presented in Table 4.5.1c indicates that the mean values of formal and informal
knowledge sharing are between 4.35–4.46 and 4.42–4.61, respectively. These values,
respectively, had standard deviations between 1.26-1.38 and 1.25-1.35, between the highest
and lowest scores. The overall of the mean and standard deviation composite scales of this
construct are 4.48 and 1.09 respectively. This would suggest that workers in the sample
practiced formal and informal knowledge sharing activities within firms in the HKCI.
4.4 Preliminary analysis
An evaluation on normality of distribution for constructs is needed to identify that they meet
the assumptions of regression before running multiple regression in SPSS (Pallant 2007; coakes,
Steed and Ong, 2010). Skewness and kurtosis show the state of distribution with intervals and
proportionate levels of information. Several guidelines suggest that, if the distribution of a
variable is symmetrical, then the result for skewness and kurtosis are zero, subject to whether
93
the observed distribution is normal (Bagozzi and Baumgartner, 1994; Gliner, 2011). Positive
values for skewness demonstrate a positive skew, wherein positive values for kurtosis pinpoint a
distribution with less variability (leptokurtic). On the contrary, negative values for skewness
demonstrate a negative skewness, whereas negative values for kurtosis indicate highly
dispersed distributions (playkurtic). Further descriptive statistics, such as measures of variability
and central tendency, can likewise be utilized to determine the normality of the distribution.
4.5 Skewness and Kurtosis
The values of skewness and kurtosis demonstrating the state of distribution are utilized for
testing normality (D’Agostino and Pearson, 1973; Bowman and Shenton ,1975; Peason et al.,
1977). Skewness and kurtosis values in the range of between +2 and -2 demonstrate the
normality of the data distribution (Gliner, 2011, Coakes, 2013). Referring to Table 4.6, the
negative values of skewness that were found in items of Knowledge Sharing within +/-2 can be
considered acceptable and is an indication of normality (Coakes, 2013). All variables are slightly
skewed to the right with ranging from- .05 to -.675, with a relatively long tail in the recurrence
distribution curve. A negative skewness trends to the right side (Hair et al., 2001), implying that
they are approximately typically distributed. Although they are on the right side of the curve,
they do not significantly veer off from normality.
The kurtosis statistic of all variables indicated a normal curve ranging from- .205 to -.634 except
for trust, which is at .029, and of these, values within +/-2 are considered acceptable (Hair at el.,
2006; Coakes, 2013). Tabachnick and Fidell (2007, p.81) suggested inspecting the shape of the
distribution by a histogram allows for further examination of normality of distribution. Kurtosis
is used to reveal the “peakedness” or “flatness” of the distribution, whereas skewness refers to
the unbalanced state (i.e. shifting to one side whether left or right) compared with normal
94
distribution (Hair et al., 2016). The distribution of a normal curve was bell-shaped based in the
histogram, thus indicating that the scores on the variables are normally distributed (Gliner et al.,
2011).
4.6 Test of distribution normality
Table 4.6a Tests of Normality
N Mean Std.
Deviation
Skewness Kurtosis
Statistic Statistic Std.
Error
Statistic Statistic Std.
Error
Statistic Std.
Error
ATW 119 4.4118 .12611 1.37565 -.333 .222 -.606 .440
AIS 119 4.5613 .12040 1.31337 -.330 .222 -.634 .440
AT 119 4.8499 .10906 1.18971 -.675 .222 .029 .440
AK 119 4.4824 .09950 1.08547 -.056 .222 -.205 .440
Valid N
(listwise)
119
Next, the normality of data for each construct was examined. The distribution of data must
correspond to a normal distribution to achieve normality (Hair et al. 2006). The normality
assumption is assessed to investigate the approximate distribution of the observed variables (by
examining statistics such as histogram, stem-and leaf-plots, boxplot, detrended normal plots,
skewness and kurtosis) (Bagozzi and Baumgatner, 1994), as well as figures, such as normal
probability plots of ordinary, studentized, or Jackknife residuals. Furthermore, goodness-of-fit
tests, such as the Kolmogorov–Smirnov test (Stephens, 1974, Looney et al.1985), and, in the
case of small sample sizes (e.g., n<50), the Shapiro–Wilks (1965) test, can also be performed.
95
The results from statistical data analysis are presented in Table 4.6.1a. To examine normality
with detrended probability plots and the normal probability, the Kolmogorov–Smirnov statistic
is used with a Lilliefors significance level in this study. As all statistical data are assumed to be
greater than .05, this indicates that all data are at a significant level. Furthermore, as the
sample size is more than 100 in this research, the results of the Shapiro–Wilk statistics are also
significant.
Figure 4.6 Summary of Histograms for all Variables in the Model
96
*ATW = Average Training for workers *AIS= Average incentive systems *AT= Average Trust *AQ= Family control business (FCBs) *AK= Average knowledge sharing Descriptive data of FCBs and Non-FCBs in the HKCI
Insights on knowledge-sharing practices in the HKCI may be obtained from the distinct effects of
FCBs and Non-FCBs. The two groups of demographic data of FCBs and Non-FCBs in HKCI are
presented in Table 4.6. As shown in Table 4.6, some significant differences were found between
FCBs and Non-FCBs in relation to business performance. FCBs performed better than Non-FCBs
in terms of average sales of more than HK$10 million per year, and FCBs did relatively well in
terms of increase in sales from 10%-70%. However, other demographic data did not offer any
conclusive insights to clearly demonstrate the influence of FCBs on knowledge-sharing practices
in the HKCI-related industries. In terms of the percentage increase in labour productivity (the
contribution of on-duty workers) in the past three years, not much difference in performance
was found between FCBs and Non-FCBs.
97
Table 4.6b Descriptive Analysis of FCBs and Non-FCBs in the HKCI
` Measurement Items
Response
Percent
Response
Count
(Total
responses: 119)
Response
Percent
Response
Count
(Total
responses: 119)
1.CEO 0 0 11.5 6
2. GENERAL 22.4 15 9.6 5
3. MANAGEING
DIRECTOR 17.9 12 15.4 8
4. COO 0 0 1.5 1
5. Others 46.3 31 59.6 31
1. =or <1 19.4 13 27 14
2. >1 to 5 17.9 12 17.3 9
3. >5 to 10 10.4 7 1.9 1
4. >10 to 15 10.4 7 13.5 7
5. >15 41.8 28 40.4 21
1. =or <50 44.8 30 57.7 30
2. 51-200 13.4 9 9.6 5
3. 201-1,000 20.9 14 9.6 5
4. 1,001-3,000 9 6 1.9 1
5. > 3,000 11.9 8 19.2 10
1. Manufacturing 40.3 27 28.9 15
2. Product (owned 20.9 14 30.8 16
3. Product Trading 25.4 17 21.2 11
4. Service (material 13.4 9 19.2 10
1. = or < 1 26.9 18 36.5 19
2. >1 to 10 25.4 17 23.1 12
3. >10 to 50 13.4 9 5.8 3
4. > 50 to 100 14.9 10 13.5 7
5. > 100 19.4 13 21.2 11
1. = or < 10% 64.2 43 73.1 38
2. >10% to 40% 29.9 20 21.2 11
3. >40% to 70% 4.5 3 1.9 1
4. >70% -100% 1.5 1 3.8 2
5. > 100% 0 0 0 0
1. = or < 10% 70.2 47 76.9 40
2. >10% to 40% 23.9 16 21.2 11
3. >40% to 70% 4.5 3 0 0
4. >70% -100% 1.5 1 1.9 1
5. > 100% 0 0 0 0
The% increase of labour
productivity ( the revenue
contributed by an on-duty workers
) in the past 3 years.
Position in your enterprise
Number of year(s) your enterprise
has been operating?
Family
(52 resondents)(67 repondents)
Non-Family
Number of employees?
Business Category that your firm
belongs to the clothing industry
Average sales per year in the past
3 years (HK$ million(s))?
The % increase of sales in the past
3 years
LEE, Yuk Ling Angie Student Number: C3173954
98
4.7. Summary of descriptive data
The analysis of the independent variables shows that the mean values of all measured
constructs were high (ranging between 4.89 and 5.04 on a seven-point Likert scale).
The outcomes of Knowledge-Sharing factors demonstrated that employees in the HKCI
may effectively share and gather knowledge among their peers and managers the
following section introduces the tests of correlations among the variables and checks
the validity and reliability of the constructs.
4.8 Reliability and validity of measured data
To test the theoretical constructs in the model, reliability and validity tests were
carried out. The relationship between reliability and validity can be treated as true
score model (Malhotra, 2010). Theoretical constructs can only be measured through
detectable measures or indicators that determine the full theoretical meaning of the
core construct; thus, multiple indicators of a construct are required (Steenkamp and
Baumgartner, 2000). Both validity and reliability are observed in the current study by
using SPSS, as it allows researchers to test the impacts of dormant variables on
observed variables and to determine the partial error (Baumgartner and Homburg,
1996: Bollen, 1989).
4.8.1. Validity of measured data
Before the hypothesis test, factor analysis was utilized in this study to examine the
validation of variables through key component analysis. The tests were run using SPSS
to analyse the correlations among the key variables (incentive systems, training for
workers, knowledge sharing, trust, and FCBs). This key variable is purposefully
produced by SPSS for subsequent multiple regression analyses. The criteria and
technique of measurements are clarified below.
LEE, Yuk Ling Angie Student Number: C3173954
99
1. Principal component analysis was used as a technique for factor extraction.
Eigenvalues of (> 1.0) identify the amount of differences in the variables accounted by
a specific factor. A component was framed as a solitary variable, as its Eigenvalue was
higher than 1, thus meeting the accompanying two criteria.
2. Kaiser–Meyer Olkin (KMO) was used to gauge different critical relationships
among various variables (Kaiser, 1974). KMO was calculated as a measure somewhere
around 0 and 1, wherein an estimation of near 1 indicates an abnormal state of
correlation between variables. Tabachnick and Findell (2007), as referred to in
Williams et al., 2010, recommended that outcomes larger than .50 represent a
satisfactory rate of correlation.
3. Bartlett's test of sphericity was utilized to examine the significance of the
components (smaller than .05), which were distinguished in the component analysis.
4.8.2. Validity of independent and dependent variables
The validity of independent and dependent constructs was examined using KMO,
which measures the sample adequacy (check), and Barlett's test of sphericity was
adopted to examine the variables (incentive systems, training for workers, trust,
knowledge sharing, and FCBs). As demonstrated in Table 4.9.2a and 4.9.2b, KMO tests
have the estimation of equivalent to and higher than the measure (>.50 or equivalent
to .50) and the significant value of Barlett's test of Sphericity is .00 (paradigm smaller
than .05). The outcomes indicate that the gathered data of independent and
dependent variables are critical in the test and are acceptable for further factual
analysis.
Meanwhile, the results of factor analysis affirm that FCBs and KNOWLEDGE SHARING
(formal and informal) had "component loadings" larger than .50. In this way, the
individual key components for independent and dependent variables contributed to
LEE, Yuk Ling Angie Student Number: C3173954
100
their respective constructs and were framed as a solitary variable separately (Hair et
al., 2006)
Table 4.8.2 Factor Analysis of Variables
Factors Items
Component
Matrix
Cronbach's
Alpha KMO
Barlett's
Test (Sig)
Ability ( Training for
workers)
TW1 .865
.939 .892 .000
TW2 .917
TW3 .923
TW4 .883
TW5 .901
Motivation ( Incentive
Systems)
IS1 .833
.945 .880 .000
IS2 .915
IS3 .909
IS4 .902
IS5 .918
Oportunity ( Trust)
T1 .910
.947 .900 .000
T2 .898
T3 .847
T4 .888
T5 .892
T6 .907
FCBs FMP .875
.690 .500 .000 FOE .875
Knowledge sharing
(Formal and Informal)
K1 .769
.950 .913 .000
K2 .819
K3 .811
K4 .779
K5 .885
K6 .849
K7 .877
K8 .840
K9 .815
K10 .861
LEE, Yuk Ling Angie Student Number: C3173954
101
4.9. Reliability analysis
Reliability reflects the extent to which an indicator is free from random errors
(Diamantopoulos and Siguaw, 2000; Malhotra, 2007; Hair et al., 2009).
A typical statistical approach to test internal consistency would be the Cronbach’s
alpha test (Shin et al., 2000). Alpha values <.60 are thought to be weak, whereas
values (the correlation scores) ≥.7 are considered robust for this research (Nunnally,
1978). Results of the reliability tests to measure training for workers, trust, knowledge
sharing and incentive systems are presented below in Table 4.8.2.
4.9.1. Ability (Training for Workers)
As shown in Table 4.8.2, the alpha value of training for workers was .939, higher than
the criterion value (≥.7) suggested by Nunnally (1978). The analysis shows that the
gathered data is robust, and that this item scale (TW1-5) has a strong internal
reliability for further statistical analysis. As stated by Lubans et al. (2010), the scale has
more items than necessary or has been repeating in the scale for reliability test if the
alpha value is >.9. Accordingly, some repetitive items in this scale might be reduced in
further studies.
4.9.2. Motivation (Incentive Systems)
As shown in Table 4.8.2, the alpha value of incentive systems was .945, higher than the
criterion value (≥.70) suggested by Nunnally (1978). This outcome demonstrates that
the data collected are statistically robust, and that this item scale (IS1-5) has solid
inner dependability for further measurable examination. As per Leech et al. (2010), an
alpha value >.9 indicates that the items in the scale are monotonous for the reliability
LEE, Yuk Ling Angie Student Number: C3173954
102
test, or that the scale has a larger number of items than necessary. Accordingly, some
redundant items in this scale might be excluded in further studies.
4.9.3. Opportunity (Trust)
As shown in Table 4.8.2, the alpha value of trust was .947, thus meeting the criterion
(≥0.70) suggested by Nunnally (1978). This outcome demonstrates that the data
gathered are statistically robust, and that the (T1-5) item scale has strong internal
reliability for further statistical analysis. As per Leech et al. (2010), an alpha value >.9
indicates that the items in the scale are monotonous for the reliability test, or that the
scale has a larger number of items than necessary. Accordingly, some tedious items in
this scale might be excluded in further studies.
4.9.4. Knowledge Sharing
The measurement items of knowledge sharing, including formal and informal
knowledge sharing were tested for Cronbach’s alpha reliability test. The alpha values
of formal and informal knowledge sharing were both over .95, thus meeting the
criterion of high reliability (>.70) suggested by Nunnally (1978). As indicated in Table
4.8.2, the data collected are statistically robust, and the items scale (K1-10) has a
satisfactory internal reliability for undertaking future statistical analysis.
Further analysis of the independent variables revealed a high level of correlation
among trust, incentive systems, and training for workers. The Pearson’s correlation
coefficient of .680 between knowledge sharing and trust is shown in Table 4.9.5b.
While Pearson’s correlation of .649 was found between knowledge sharing and
incentive systems at a significance level of .05 vs. .01 level in both participations in
trust and incentive systems were highly positively related to knowledge sharing and
training for workers.
LEE, Yuk Ling Angie Student Number: C3173954
103
The significant correlations between the supporting AMO model may provide some
indications of the correlations among ability (training for workers), motivation
(incentive systems), opportunity(trust), and knowledge sharing as well as FCBs;
specifically, incentive systems and trust were more highly associated to knowledge
sharing. As stated in Chapter 3, the impact of AMO model on knowledge sharing will
be used for hypothesis testing in the following section.
4.9.5 Discriminant and Construct validity
Discriminant validity refers to the degree to which two variables are statistically
distinct from each other (Yau et al. 1998; Malhotra 2009). Discriminant validity may be
achieved when various latent factors via a cross-correlation between the various
indicators individually are not too high, just fairly strong (Kline 1998; Abbad, Morris
and De Nahlik 2009). The acceptable cut off level (the correlation coefficient), r of .85
is accepted generally for evaluating discriminant validity (Hultén 2007). Moreover, the
correlations between indicators must not be over their reliability estimates, i.e. the
coefficient alpha of each scale (Gaski and Nevin 1985; O'Cass and Grace 2008).
As such, the coefficients of correlation between Cronbach’s Alpha of the measured
constructs were shown in Table 4.9.5b. The correlation coefficients are shown in the
diagonal of lower matrix. Each of these coefficients were not over .85, so all
constructs were not significantly correlated. Further, the bolded values, namely
Cronbach’s Alpha, were on the diagonal, and they exceeded the correlation
coefficients shown in Table 4.9.5b. The above evidence verified construct validity.
LEE, Yuk Ling Angie Student Number: C3173954
104
Table 4.9.5 Correlations of Factors in this Study
Correlations
AQ ATW AIS AT AK
AQ
Pearson Correlation 1
Sig. (2-tailed)
N 119
ATW
Pearson Correlation .133 1
Sig. (2-tailed) .148
N 119 119
AIS
Pearson Correlation .129 .766** 1
Sig. (2-tailed) .162 .000
N 119 119 119
AT
Pearson Correlation .155 .633** .734** 1
Sig. (2-tailed) .093 .000 .000
N 119 119 119 119
AK
Pearson Correlation .045 .592** .649** .680** 1
Sig. (2-tailed) .626 .000 .000 .000
N 119 119 119 119 119
** Correlation is significant at the .01 level (2-tailed).
Based on the results from this preliminary analysis, all measured constructs achieved
satisfactory validity and reliability and fulfilled regression analysis assumptions. On this
basis, they are suitable for hypotheses testing, which is discussed in the next section.
4.10. Hypothesis Testing
As indicated earlier in Chapter 3, the study’s six hypotheses were tested. After
carrying out tests for validity and reliability, the data collected were subjected
to hypothesis testing, and the results are shown in Table 4.10a.
LEE, Yuk Ling Angie Student Number: C3173954
105
Table 4.10a Multicollinearity Test Results in Model 1
Coefficients
a. Dependent Variable: DV: Knowledge Sharing
The result of Model 1 (i.e., Training for Workers, Incentive Systems, and Trust)
are significant, VIF was not significant as VIF is between 2.505 to 3.268 which
is less than 10. Hence there are no multicollinearity issues and the null
hypothesis is rejected.
Standardized
Coefficients
B Std. Error Beta Tolerance VIF
(Constant) 1.151 0.290 3.976 0.000
Scale: Worker for Training
(TW1-TW5)
0.140 0.078 0.177 1.794 0.075 0.399 2.505
Scale: Incentive Systems
(IS1-IS5)
0.199 0.093 0.240 2.133 0.035 0.306 3.268
Scale: Trust (T1-T6) 0.373 0.085 0.409 4.394 0.000 0.448 2.233
1
Model
Unstandardized Coefficients
t Sig.
Collinearity Statistics
LEE, Yuk Ling Angie Student Number: C3173954
106
Table 4.10b Multicollinearity Test Results in Model 2
Coefficients
*p<.10, **p<.05, ***p<.01
The second model presented totally different outcomes as a result of the
interactions (i.e. training for workers, incentive systems, and trust). The VIF
values were not significant except for trust, thus causing a multicollinearity
problem. Most common misunderstanding that exists here is that X
(independent variable) and M(Moderator) are likely to be highly correlated
with XM and thus an estimation problem may be created by multicollinearity
which results in poor estimates of regression coefficients, big standard errors,
and reduced power of the statistical tests of the interaction. To address the
multicollinearity issue, the Process Macro for SPSS for Linear Regression
Analysis was used (Hayes, 2013). Process Model 2 was used, with three runs
undertaken to establish the moderation effects across the model.
In order to address the issue of high multicollinearity, a bootstrap analysis was
generated with 1,000 iterations for bias-correction. To test whether FCBs
moderate the effect of AMO factors on knowledge sharing (KS), the model for
KS was built in steps. KS was then estimated from ATW_x_AQ (Training and
Standardized
Coefficients
B Std. Error Beta Tolerance VIF
(Constant) 1.084 0.297 3.649 0.000
Scale: Training for workers
(TW1-TW5)
0.085 0.153 0.107 0.554 0.581 0.103 9.701
Scale: Incentive System (IS1-
IS5)
0.371 0.187 0.449 1.979 0.050 0.075 13.279
Scale: Trust (T1-T6) 0.311 0.147 0.341 2.117 0.037 0.149 6.690
Interaction: AQ (FCB) x ATW
(Worker Training)
0.042 0.083 0.242 0.502 0.617 0.017 59.830
Interaction: AQ (FCB) x AIS
(Incentive System)
-0.090 0.087 -0.528 -1.030 0.305 0.015 67.891
Interaction: AQ (FCB) x AT
(Trust)
0.029 0.064 0.171 0.456 0.649 0.028 36.128
2
Model
Unstandardized
Coefficients
t Sig.
Collinearity Statistics
LEE, Yuk Ling Angie Student Number: C3173954
107
FCBs) and for any additional variables of interest other than ATW_x_AQ, we
created various covariates and so forth. Independent variable (X) and
Moderator (M) were added. Furthermore, two other AMO factors used as
covariates that were plugged into the model along with ATW_x_AQ. The
convention is to use the means of the covariates. The value plugged into the
model for the covariates ended up merely adding or subtracting from the
regression constant, depending on the signs of the regression coefficients for
the covariates, thus affecting the overall KS results. Hypothesis 2 (H2.1, H2.2,
and H2.3) was examined by Process Macro in SPSS after Hypothesis 1 (H1.1,
H1.2 and H1.3).
For testing Hypothesis 1 (H1.1, H1.2 and H1.3), the univariate linear regression
analysis was utilized to test the impact of independent variables (i.e., training for
workers, incentive system and trust) on the dependent variable (i.e., knowledge
sharing) and the moderating effect of FCBs. Figure 4.10 below presents the main
hypothesized relationships.
Figure 4.10. Operational Model: Key Hypothesized Relationships between AMO
Factors and Knowledge Sharing.
LEE, Yuk Ling Angie Student Number: C3173954
108
4.10.1 Hypothesis 1.1
H1.1 proposes that training for workers is positively related to knowledge
sharing in the HKCI. Menkhoff et al. (2005) reported the prevalence of
knowledge-sharing practices and training for workers within firms.
Figure 4.10.1: Operational Model for Ability (Training for Workers) and
Knowledge Sharing
The result suggests that training for workers has a marginal and moderately
significant impact on knowledge sharing, and that such influence is positive
(b= .140, p>.10), and the results shown in Table 4.10a. Thus, H1.1 is
supported.
4.10.2 Hypothesis 1.2
H1.2 proposes that incentive systems are positively associated with
knowledge sharing in the HKCI. Rewards can motivate employees to share
their knowledge within an organizational or a group setting. Rewards can
come in the form of monetary incentives (extrinsic) and non-monetary
(intrinsic) rewards (Bartol and Srivastava, 2002). The Operational model
tested for this hypothesis is shown in Figure 4.10.2 below.
LEE, Yuk Ling Angie Student Number: C3173954
109
Figure 4.10.2: Operational Model for Motivation (Incentive systems) and
Knowledge Sharing
The result suggests that incentive systems have a positive influence on
knowledge sharing (b= .199, p<.05), and the results are shown in Table 4.10a.
Thus, H1.2 is supported.
4.10.3 Hypothesis 1.3
H1.3 proposes that Trust is positively associated with knowledge sharing in
the HKCI (Figure 4.10.3).
Figure 4.10.3: Operational Model for Opportunity (Trust) and Knowledge
Sharing.
The results demonstrate that trust has a positive impact on knowledge
sharing. Trust has a positive influence on knowledge sharing (b=.373, p<.001),
and the results shown in Table 4.10a. Thus, H1.3 is supported.
LEE, Yuk Ling Angie Student Number: C3173954
110
Furthermore, when knowledge sharing was evaluated by AMO model, with
the result of (R²=.56 explains that AMO could be statistically significant in
interpreting 56 percent of variance towards knowledge sharing. (Shown in
Table 4.10.3)
Table 4.10.3 Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .747a .558 .546 .73127
a. Predictors: (Constant), AT, ATW, AIS
4.11. Process macro in SPSS for B analysis
The moderating effect can be tested by Process Macro in SPSS (Hayes, 2013).
In the current research, FCBs has a moderating effect between AMO factors
and KS, in that FCBs and each of the AMO factors interact and influence KS.
Such interaction was examined in the current study. The outcomes helped
identify the moderating effect of knowledge sharing.
Referring to Table 4.10b, the coefficients indicates multicollinearity scores. VIF
of Model #1 for each AMO factor is within tolerance. Model #2, which
includes interactions input results in a very high VIF for each scale (e.g. ATW
9.7 is high and nearly at the edge of the tolerance; AIS 13.27 is the highest and
out of tolerance; only the VIF values for trust of 6.69 is in the safe zone). The
high VIF out of tolerance zones is considered risky and it may impede the
process of obtaining reliable regression analysis results (Belsley et al., 1980;
Hair et al., 1995, O’Brien, 2007). Therefore, the process macro adopted a
bootstrap analysis, using 1,000 iterations and bias-correction to solve the
problems of high multicollinearity.
LEE, Yuk Ling Angie Student Number: C3173954
111
4.11.1. Hypothesis 2.1
As shown in Figure 4.11, H2.1 proposes that FCBs act as a moderating factor in
the correlation between training for workers and knowledge sharing.
Figure 4.11.1 Operational Model for Ability (Training for Workers) and FCBs
Table 4.11.1a Model Summary in between training for workers and FCBs
Looking first to H2.1, the regression analysis result shows the moderating
impact of FCBs in Table 4.11.1a, on training for workers (ATW) and knowledge
R R² F df1 df2 p
Outcome: AK .76 .58 36.80 5.00 112.00 .00
Model = 2
b se t p LLCI ULCI
constrant 1.70 .45 3.83 .00 .82 2.59
FCBs -.04 .06 -.72 .47 -.15 .07
Training for workers .12 .09 1.23 .22 -.07 .30
FBCs x Training for
workers -.10 .05 -2.07 .04 -.20 .00
Y= Knowledge
sharing
X= Training for
workers M= FCBs
LEE, Yuk Ling Angie Student Number: C3173954
112
sharing, and is represented by the regression coefficient for the interaction of
FCBs and ATW (termed as ATW x AQ). Such a coefficient is negative and
statistically significant (i.e.²=.58, b= –.10, t (112) = –2.07, p=.04<.05,) Thus, the
impact of ATW on KS depends on FCBs. However, the relationship is negative,
thus implying that training for workers has a negative impact on knowledge
sharing in FCBs. (R² change=.02) which represents an increase of 2%
following the interaction term.
Table 4.11.1b Conditional effect of Training for workers (X) and Knowledge
sharing (Y) at values of FCBs (M)
In order to interpret the moderating effect, the simple slopes were examined.
When the values for FCBs are low, there is a significant positive relationship
between training for workers and knowledge sharing (b=.22, p=.04<.05).
When FCBs are average, there is no significant relationship. (b=.12,
p=.22>.05). Similarly, when FCBs are high, there is a non-significant
relationship between incentive systems and knowledge sharing (b=.00,
p=.98>.05). These results tell us that the relationship between training for
workers and Knowledge sharing only really emerges in firm with low levels of
FCBs.
FCBs Effect se t p LLCI ULCI
Low -1.01 .22 .11 2.07 .04 .01 .43
Average .00 .12 .09 1.23 .22 -.07 .30
High 1.14 .00 .11 -.02 .98 -.22 .22
LEE, Yuk Ling Angie Student Number: C3173954
113
Table 4.11.1c Conditional effect of Training for workers(X) on Knowledge sharing (Y) at values of FCBs (M) (Johnson-Neyman significance region(s))
FCBs Effect se t P LLCI ULCI
-1.01 .22 .11 2.07 .04 .01 .43
-.86 .20 .10 1.98 .05 .00 .41
-.81 .20 .10 1.95 .05 .00 .40
-61 .18 .10 1.81 .07 -.02 .37
-.41 .16 .10 1.65 .10 -.03 .35
-.21 .14 .09 1.46 .15 -.05 .32
-.01 .12 .09 1.25 .21 -.07 .30
.19 .10 .09 1.02 .31 -.09 .28
.39 .08 .10 .79 .43 -.11 .27
.59 .06 .10 .56 .58 -.14 .25
.79 .03 .10 .34 .74 -.17 .24
.99 .01 .11 .13 .90 -.20 .22
1.19 -.01 .11 -.06 .95 -.23 .21
1.39 -.03 .12 -.23 .82 -.26 .20
1.59 .-05 .12 -.39 .70 -.29 .'20
1.79 .-07 .13 -.53 .60 -.33 .19
1.99 .-09 .14 -.65 .52 -.36 .18
2.19 -.11 .14 -.76 .45 -.40 .18
2.39 -.13 .15 -.86 .39 -.43 .17
2.59 -.15 .16 -.94 .35 -.47 .17
2.79 -.17 .17 -1.02 .31 -.50 .16
2.99 -.19 .18 -1.09 .28 -.54 .16
Refer to the Label 4.11.1c, the output of the John-Neyman method provides another
approach to explain the simple slopes result. Looking at the b-values we can see that
the relationship between training and knowledge sharing emerges only when FCBs
scores are low. The result is consistent with Table 4.11.1b- conditional effect of
Training for workers (X) and Knowledge sharing (Y) at values of FCBs (M).
4.11.2. Hypothesis 2.2
LEE, Yuk Ling Angie Student Number: C3173954
114
As shown in Table 4.11.2, H2.2 proposes that FCBs act as a moderating factor
in the correlation between incentive systems and knowledge sharing.
Figure 4.11.2 Operational Model for Motivation (Incentive Systems) and
FCBs
Table 4.11.2a Model Summary in between incentive systems and FCBs
The regression analysis for H2.2 states the moderating impact of FCBs on
incentive systems (AIS) and knowledge sharing, is represented by the
R R² F df1 df2 p
Outcome: AK .77 .59 .51 38.80 112.00 .00
Model = 2
b se t p LLCI ULCI
constrant 2.22 .56 3.94 .00 1.10 3.33
FCBs -.04 .06 -.78 .43 -.16 .07
Incentive systems .22 .12 1.88 .06 -.01 .45
FBCs x Incentive
systems -.12 .05 -2.55 .01 -.22 -.03
Y= Knowledge
sharing X= Incentive systems M= FCBs
LEE, Yuk Ling Angie Student Number: C3173954
115
regression coefficient for the interaction between FCBs and AIS (termed as AIS
x AQ). Such a coefficient is negative and statistically significant (i.e., R²=.59,
b= -.12, t (112) =-2.55, p=.01<.05,) Thus, the impact of AIS on KS is moderated
by FCBs. However, as the interaction term is negative, it implies that with low
levels of FCBs, incentive systems will have a positive impact on knowledge
sharing. (R² change=.03) which represents an increase of 3% after
interaction term.
Table 4.11.2b Conditional effect of Incentive systems (X) and Knowledge
sharing(Y) at values of FCBs (M)
When level of FCBs are low, there is a significant and positive relationship
between incentives and knowledge sharing (b=.34, p=.01<.05). When FCBs are
average, there is a non- significant relationship between incentives for
workers and knowledge sharing. (b=.22, p=.06>.05). Similarly, when the FCBs
value is high, there is a non-significant relationship as well (b=.08, p=.51>.05).
These results tell us that the relationship between incentive systems and
knowledge sharing emerges only with lower levels of FCBs.
FCBs Effect se t p LLCI ULCI
Low -1.01 .34 .14 2.53 .01 .07 .61
Average .00 .22 .12 1.88 .06 -.01 .45
High 1.14 .08 .12 .66 .51 -.15 .31
LEE, Yuk Ling Angie Student Number: C3173954
116
Table 4.11.2c Conditional effect of Incentive systems (X) on Knowledge sharing(Y) at values of FCBs (M) (Johnson-Neyman significance region(s)
FCBs Effect se t P LLCI ULCI
-1.01 .34 .14 2.53 .01 .07 .61
-.81 .32 .13 2.44 .02 .06 .58
-61 .29 .13 2.33 .02 .04 .54
-.41 .27 .12 2.21 .03 .03 .51
-.21 .24 .12 2.06 .04 .01 .48
-.12 .23 .12 1.98 .05 .00 .47
-.01 .22 .12 1.89 .06 -.01 .45
.19 .20 .11 1.71 .09 -.03 .42
.39 .17 .11 1.51 .13 -.05 .40
.59 .15 .11 1.29 .20 -.08 .37
.79 .12 .11 1.07 .29 -.10 .35
.99 .10 .12 .84 .40 -.13 .33
1.19 .07 .12 .61 .54 -.16 .30
1.39 .05 .12 .39 .70 -.19 .29
1.59 .02 .12 .18 .86 -.22 .27
1.79 .00 .13 -.02 .99 -.26 .25
1.99 -.03 .13 -.20 .84 -.29 .24
2.19 .-05 .14 -.37 .71 -.33 .22
2.39 -.08 .14 -.53 .60 -.36 .21
2.59 -.10 .15 -67 .50 -.40 .20
2.79 -.13 .16 -.80 .43 -.44 .19
2.99 -.15 .16 -.92 .36 -.48 .17
Looking at the b-values shown on Table 4.11.2c, John-Neyman method
provides statistical b-values that the relationship between incentive systems
and knowledge sharing emerges only when FCBs scores are low. The result is
consistent with Table 4.11.2b –conditional effect of Incentive systems (X) and
Knowledge sharing (Y) at values of FCBs (M).
LEE, Yuk Ling Angie Student Number: C3173954
117
4.11.3. Hypothesis 2.3 (H2.3)
As shown in Table 4.11.3, H2.3 proposes that FCBs positively act as a
moderating factor in the relationship between trust and knowledge sharing
(Figure 4.11.3).
Figure 4.11.3 Operational Model for Opportunity (Trust) and FCBs
Table 4.11.3a Model Summary In between Trust and FCBs
R R² F df1 df2 p
Outcome: AK .77 .59 44.11 5.00 112.00 .00
Model = 2
b se t p LLCI ULCI
constrant 2.79 .51 5.42 .00 1.77 3.81
FCBs -.03 .06 -.53 .59 -.14 .08
Trust .28 .11 2.51 .01 .06 .50
FBCs x Trust -.17 .06 -2.87 .00 -.29 -.05
Y= Knowledge
sharing X= Trust M= FCBs
LEE, Yuk Ling Angie Student Number: C3173954
118
The regression analysis for H2.3 states the moderating impact of FCBs on trust
(AT) and knowledge sharing, is represented by the regression coefficient for
the interaction of FCBs and Trust (termed as AT x AQ). Such a coefficient is
negative and statistically significant (i.e. R²=.59, b= -.17, t (112) =-2.87,
p=.00<.01). Thus, the impact of AT on KS is moderated by FCBs. However, as
the interaction effect is negative, it implies that only when the values of FCBs
are low, the relationship between trust and knowledge sharing is positive. (R²
change=.3) which represents a 3% increase after the interaction effects.
Table 4.11.3b Conditional effect of Trust (X) and Knowledge sharing (Y) at
values of FCBs (M)
When level of FCBs are low, there is a significant and positive relationship
between trust and knowledge sharing (b=.45, p=.00<.01). When FCBs are
average, there is significant positive relationship as well (b=.28, p=.01<.05).
When FCBs are high, there is a non-significant relationship between trust and
knowledge sharing (b=.08, p=.59>.05). These results tell us that the significant
and positive relationship between trust and knowledge sharing only really
emerges in firm with low or average levels of FCBs.
FCBs Effect se t p LLCI ULCI
Low -1.01 .45 .10 4.49 .00 .25 .65
Average .00 .28 .11 2.51 .01 .06 .50
High 1.14 .08 .15 .54 .59 -.22 .39
LEE, Yuk Ling Angie Student Number: C3173954
119
Table 4.11.3c Conditional effect of Trust (X) on Knowledge sharing (Y) at
values of FCBs (M)
(Johnson-Neyman significance region(s)
FCBs Effect se t p LLCI ULCI
-1.01 .45 .10 4.49 .00 .25 .65
-.81 .42 .10 4.18 .00 .22 .62
-.61 .38 .10 3.81 .00 .18 .59
-.41 .35 .10 3.40 .00 .15 .56
-.21 .32 .11 2.97 .00 .10 .53
-.01 .28 .11 2.53 .01 .06 .50
.19 .25 .12 2.12 .04 .02 .48
.26 .24 .12 1.98 .05 .00 .47
.39 .21 .12 1.73 .09 -.03 .46
.59 .18 .13 1.37 .17 -.08 .44
.79 .15 .14 1.05 .30 -.13 .42
.99 .11 .15 .75 .45 -.18 .40
1.19 .08 .16 .49 .62 -.23 .39
1.39 .04 .17 .26 .80 -.29 .37
1.59 .01 .18 .05 .96 -.34 .36
1.79 -.03 .19 -.14 .89 -.39 .34
1.99 -.06 .20 -.31 .76 -.45 .33
2.19 -.09 .21 -.46 .65 -.50 .31
2.39 -.13 .22 -.60 .55 -.56 .30
2.59 -.16 .23 -.72 .47 -.61 .29
2.79 -.20 .24 -.83 .41 -.67 .27
2.99 -.23 .25 -.93 .35 -.72 .26
LEE, Yuk Ling Angie Student Number: C3173954
120
Refer to the Table 4.11.3c, the output of the John-Neyman method provides
another approach to explain the simple slope results. Looking at the b-values
we can see that the relationship between trust and knowledge sharing
emerges only when the FCBs scores are low. The result is consistent with
Table 4.11.3b–conditional effect of Trust (X) and Knowledge sharing (Y) at
values of FCBs (M).
4.12 Simple slope analysis
Figure 4.12 Simple Slope Result for AMO Factors and FCBs
LEE, Yuk Ling Angie Student Number: C3173954
121
As shown in Figure 4.12, H2.1, H2.2, and H2.3 propose that FCBs acts as a
moderating factor influencing the effects of training for workers, incentive
system, and trust on knowledge sharing. For average levels of FCB, there is
not much impact noted. However, for lower values of FCBs (family members
involvement levels), a significant and positive relationship between the AMO
factors and knowledge sharing is observed. The results are consistent as for
these conditional effects of X on Y values through the moderator as clearly
shown in the Johnson-Neyman test.
4.13 Summary of hypothesis testing
The empirical results of the data analysis confirm that H1.1, H1.2, and H1.3 are
positively associated with having a direct effect on the knowledge sharing. H2.1, H2.2,
and H2.3 are significant as confirmed by the linear regression analysis results. As we
can see, this additional variable (FCBs), which is described as a moderator, can help
demonstrate that the knowledge sharing performance is influenced by the effect of
the other independent variable (AMO factors). Further, the findings suggest that FCBs
serve as a moderating variable between the AMO factors and knowledge sharing. This
finding is consistent with results reported by prior studies, i.e., FCBs are an additional
variable and that the characteristics of individuals influence the outcome of knowledge
sharing performance as they respond to an experimental manipulation (Baron and
LEE, Yuk Ling Angie Student Number: C3173954
122
Kenny,1986; Pallant, 2001). The findings and test results are summarized in Table 4.13
below.
Table 4.13: Summary of Hypotheses Test Results
*p<.10, **p<.05, ***p<.01
4.14 Chapter Summary
This research provides empirical support for the hypothesized relationships
and found both theoretical and practical contributions from this study.
Theoretically, the AMO model can be utilized to demonstrate the importance
of ability (training for workers), motivation (incentive systems for staff) and
opportunity (trust among co-workers) on knowledge sharing. More
specifically, the results of H1.1, H1.2, and H1.3 support the positive
Hypothesis Description of Hypothesis Sig. Result
H1.1 In the HKCI, Training for Workers is positively associated with Knowledge Sharing
.075 Marginal supported
H1.2 In the HKCI, Incentive Systems is positively associated with Knowledge Sharing
.035 Supported
H1.3 In the HKCI, Trust is positively associated with Knowledge Sharing
.000 Strongly Supported
H2.1 In the HKCI, FCBs acts as a moderating factor in the relationship between Ability (Training for Workers) and Knowledge Sharing
.04 Supported
H2.2 In the HKCI, FCBs acts as a moderating factor in the relationship between Motivation (Incentive Systems) and Knowledge Sharing
.01 Supported
H2.3 In the HKCI, FCBs acts as a moderating factor in the relationship between Opportunity (Trust) and Knowledge Sharing
.00 Strongly Supported
LEE, Yuk Ling Angie Student Number: C3173954
123
relationship between the AMO factors on both formal and informal
knowledge sharing processes. Although there is high multicollinearity
between all interaction variables (for moderation effect) in the regression
model, the results of H2.1, H2.2, and H2.3 proved that the moderating effects
are significant. The analysis was conducted following Process Macro steps
(Hayes, 2013). The moderator and the independent variables interact to cause
a performance change in the dependent variable (knowledge sharing
behaviour) (Baron and Kenny, 1986; Winer, 1971). Chapter 5, the final chapter
of this thesis presents further discussion and conclusion of the study’s findings
and limitations and suggestions for future research.
LEE, Yuk Ling Angie Student Number: C3173954
124
Chapter 5
Discussion and Conclusion
5.1 Introduction
This final chapter presents a discussion based on the findings of this study and
concludes with implications for theory and practice. The three independent variables,
namely, ability (training for workers), motivation (incentive systems), and opportunity
(trust), along with the moderating variable (FCB/Non-FCB firms) and a dependent
variable (formal and informal) knowledge sharing were the foci of this study. The study
adopted a questionnaire design consisting of the above constructs, which have already
been well-developed in the extant literature (Chua et al., 2004; Wong & Aspinwall,
2005; Mooradian, 2006; Zahra, 2007). The questionnaire employed in this study had a
total of 26 items.
The study’s contributions in terms of theoretical and managerial implications are
discussed in final section of this chapter, highlighting the significance of this research
and the investigated research problem. This chapter concludes by discussing the
limitations of this research and identifying areas for further study.
5.2 Major findings
As stated in Chapter 4, this research aimed at determining whether there is a
moderating influence of the impact of FCBs on key variables of the AMO model and
knowledge-sharing practices in the HKCI.
Building on Zahra et al.’s (2007) and using Salis & Williams’s (2010) AMO model, this
study investigated the moderating effects of FCBs on AMO factors and knowledge
sharing in the HKCI. The literature on human resource management highlights the
LEE, Yuk Ling Angie Student Number: C3173954
125
importance of training for workers, incentive systems, and trust factors in improving a
system’s performance (Boselie, 2010). These factors have also been found to be
positively related to the key components of knowledge-sharing performance in the
context of HKCI. The study’s research model explicitly shows that there exist two
components of knowledge sharing: informal knowledge and formal knowledge.
Accordingly, six hypotheses were tested through a quantitative approach for assessing
the impacts of FCBs, ability (training for workers), motivation (incentive systems), and
opportunity (trust) on the knowledge sharing-performance (formal and informal
knowledge) of firms in the HKCI.
5.3 Research framework
Based on the findings outlined in the previous chapter, this chapter provides a
discussion of each hypothesis. The impact among all relevant variables were tested by
analysing data using SPSS (ver. 22).
Figure 5.3 AMO Factors applied to Knowledge Sharing and are Individually Moderated
by FCBs
LEE, Yuk Ling Angie Student Number: C3173954
126
5.4. Discussion of findings
The hypothesized relationships were tested using regression analysis (Bock and Kim,
2001) and the data analysis confirms that H1.1, H1.2, and H1.3 are supported. Hence,
the relationship between ability (training for workers), motivation (providing incentive
systems), and opportunity (creating an environment of trust) and intention to share
knowledge in the HKCI is positive and significant and duly supported by the data.
(H1.1): In the HKCI, training for workers is positively related to knowledge sharing.
The results (b =.140, p = .075 <.10) indicate that training for workers. Although the
result is marginal significant it has a moderate positive influence on knowledge sharing.
Few researchers in the social sciences have demonstrated that the interaction effects
in real data commonly interpret between 1% and 3% of the variance in the dependent
variable (Campout and Peters, 1987). Thus, interactions interpreting even 1% of the
variance is also meaningful (Abelson, 1985; Evans, 1985; McClelland & Judd, 1993). To
maximize the power of detecting moderator effects, some researchers have adopted
the large sample sizes to achieve higher research reliability (Stone–Romero &
Liakhovitski, 2002). The result is similar i.e. positive as compared to earlier studies
(Aragón-Sánchez, 2003; Cabrera & Cabrera, 2005; Wong & Aspinwall, 2005), that have
argued that training programs can increase levels of employee self-efficacy positively.
Aragón-Sánchez (2003) also noted similar findings. Training in communication skills may
help employees to exchange information and knowledge effectively.
(H1.2): In the HKCI, implementing incentive systems is positively related to
knowledge sharing.
The results (b= .199, p = .035 <.05) indicate that incentive systems have a
statistically significant and positive relationship with knowledge sharing.
LEE, Yuk Ling Angie Student Number: C3173954
127
Contrary to prior study, Bock, & Kim, (2003) argued that rewards may
discourage employees towards knowledge sharing because incentive systems
may be a substitute given to staff for good individual level performance. The
result for H1.2 provides empirical evidence to support that incentive systems is
a significant predictor of knowledge sharing performance and consistent with
some of the earlier reported studies (Sharratt & Usoro, 2003; Wong &
Aspinwall, 2005), wherein it was advocated that higher levels of reward have a
greater impact on knowledge-sharing behaviour. This finding supports a
positive relationship between incentive systems and knowledge sharing in the
HKCI. Therefore, designing incentive and reward systems is vital in providing
new opportunities to learn and actualize one’s full potential ( Sharratt & Usoro,
2003; Wong & Aspinwall, 2005).
Offering rewards is an effective way to motivate employees of a firm to share their
knowledge with one another (Bartol & Srivastava, 2002). Experienced employees,
however, believed that there may be a negative attitude toward receiving benefits in
return for knowledge sharing performance as it is considered as a normal business
activity (Bock & Kim, 2001). Given the predicted impact of the perceived benefits of
knowledge sharing, incentive systems may thus be designed to encourage knowledge-
sharing behaviours. The incentive systems may include aspects such as individual
appraisal with rating systems for performance evaluation, bonus and salary increments,
promotions, and so on. The results are consistent with earlier studies and theoretical
arguments, which supports the positive correlation between incentive systems and
knowledge sharing (Wong & Aspinwall, 2005; Sharratt & Usoro, 2003).
(H1.3): In the HKCI, trust is positively related to knowledge sharing.
Similar findings exist for this factor and as such H1.2 is supported. The results show a
positive impact between trust and knowledge sharing in the HKCI. The analysed data
support the findings that trust is a critical factor influencing knowledge sharing in the
LEE, Yuk Ling Angie Student Number: C3173954
128
HKCI. It is also in line with extant literature on the topic (Chiu & Wang, 2006; Politis,
2003; Mooradian, 2006).
This is evident in the results (b= .373, p.000 < .05) and that trust has the strongest
statistical significance of individual coefficients as compared to training for workers and
incentive systems, and that trust has a statistically positive relationship with the
dependent variable–knowledge sharing.
The findings from this study suggest that trust is the strong factor that can affect
knowledge-sharing behaviour in HKCI. These results are consistent with previous
studies (Brann & Foddy, 1988; Hansen, 1999 ; Epstein, 2000; Foos et al., 2006),
especially in terms of informal knowledge sharing.
Major findings
Finding one: The relationship between the AMO factors and knowledge sharing in
HKCI firms is significant except training for workers. The results of H1.2, and H1.3
support the notion that the incentive systems and trust affect knowledge sharing.
The results of H2.1, H2.2, and H2.3 are all significant (p < .05) (Aiken and West, 1991).
The specific characteristics of FCBs differ significantly from those of non-FCBs (Judge &
Douglas, 1998; Porter & Van de Linde, 1995; Room, 1994; Srivastava, 1995). Ding et al.
(2008) stated that FCBs have clear strategic direction to influence the operational
aspects within their firms. The empirical findings of the current study support this
notion, and are consistent with the findings of prior research, which found that FCBs
positively influence knowledge sharing in HKCI.
Finding two: The b values for hypotheses (H2.1) b = -.10, (H2.2) b= -.12, (H2.3) b= -.17
indicate statically significant relationships with negative impact of the interaction
LEE, Yuk Ling Angie Student Number: C3173954
129
effects of FCBs, respectively, with the independent variables of training for workers,
incentive systems and trust and predictor variable of knowledge sharing. Therefore, it
follows that with low values of FCBs the relationship between the AMO factors and
knowledge sharing is positive and significant. This may be explained by the specific
characteristics of FCBs, namely, the centralization of control and ownership via
paternalism and personalized management in eastern characteristics (Yeung, 2014).
Refer to prior studies (Fisher & Howell, 2004; Lengnick-Hal & Moritz, 2003),
paternalism and personalized culture can create a negative impact on knowledge
sharing.
5.5 Moderating effect of FCBs
According to AMO Model (Appelbaum et al., 2000; Salis and Allan, 2008), AMO factors
influence the knowledge sharing performance. Zahra et al., 2006 found that knowledge
sharing can be influenced by the family involvement in the top management. Many
researchers have examined the FCBs moderating power effect (Aragon-sanchez, 2003;
Lin, 2007; Zahra, 2006) and suggested that FCBs may buffer the impact of AMO factors
individually. The results for hypotheses (H2.1) b = -.10, p<.01, (H2.2) b= -.12, p<.01,
(H2.3) b= -.17, P<.1 indicated statically significant relationships with negative impact for
training for workers, incentive systems and trust, in which the results from H2.1, H2.2,
H2.3 may be interpreted that knowledge sharing will be lowered by FCBs.
This is also reflected in (H2.1) wherein 58 percent of the variance (R²=. 58) in knowledge
sharing is explained. (R² change= .02) 2 percent is increased due to interactions in
between Training for workers and FCBs. Similarly, when knowledge sharing relationship
is analysed with incentive systems in (H2.2), 59 percent of the variance (R²= .59) is
explained. An R²change of .03 or 3 percent is increased due to interaction in between
Incentive systems and FCBs. The result of (H2.3) remains the same (R²=.59) as (H2.2)
towards knowledge sharing. An R2 change of .03 yields the same result as for H2.2.
LEE, Yuk Ling Angie Student Number: C3173954
130
Stone-Romero and Liakhovitski (2002) stated that the moderating effect is normally low
due to the small effect sizes, a one percent variance of a moderating effect is still
considered meaningful. Abelson (1985) and McClelland and Judd (1993) found that the
power of moderating effects may be higher subject to bigger sample sizes.
H2.1: In the HKCI, FCBs act as a moderating factor in the relationship between training
for workers and knowledge sharing.
The results support the assumption that FCBs impact on knowledge-sharing behaviour
when training for workers is provided. FCBs play a critical role in influencing knowledge-
sharing behaviour in firms. Contrary to expectation, FCBs have a negative moderating
effect such that low values of FCBs will increase the strength of the relationship
between training for workers and knowledge sharing. The result is consistent with an
earlier finding such as Kotey (2007) and Aragon-sancher (2003). The present study
results support the prior researches that the family characteristics such as paternalistic
leadership style(Chirico & Norqvist, 2010),informal and loosely structured management
(Redding, 1979, 1984) of FCBs are the reason, This approach cause the negative impact
for training performance , especially informal knowledge.FCBs tend to recruit less
competent relatives for management position because of “ family obligations” and need
to develop harmonious relations with each family member (Dholakia, 2002) .
Given that the sample effect size is small, values are typically low in the results of
moderation analyses (Aiken & West, 1991; Stone-Romero & Lobachevski, 2002). The
causal regime is structured into the data; hence, it is possible to assess the degree to
which various modelling approaches produce accurate coefficient estimates. In the
present study, using Process Macro in SPSS by bootstrapping the sample size and
increase it from 100 to 1000 (Hayes, 2013). This is based on the assumption that the
larger the sample size is, the smaller the standard errors produced. Thus, in this study,
FCBs act as a moderator in explaining knowledge sharing, that is, FCBs not only interact
LEE, Yuk Ling Angie Student Number: C3173954
131
with each independent variable, but are also impact the predictor variable of knowledge
sharing (McArthur and Nystrom, 1991).
To maintain business performance, providing training for workers, as well as
advancements in skills and technologies is likely to enhance a firm’s performance
through knowledge sharing behaviour. Providing training for workers is more critical in
FCBs than in non-FCBs as they effectively manage knowledge sharing from both a top-
down and a peer-to-peer perspective (Hansen & Oetinger, 2001).
H2.2: In the HKCI, FCBs act as a moderating factor in the relationship between
incentive systems & knowledge sharing.
This study indicates that the addition of the interaction between incentive systems and
FCBs can help explain the variance between knowledge sharing. Thus, H2.2, which is
consistent with previous findings (Zahra; 2010), is also supported.
Skulski (1996) reported that individual is reluctant to share crucial knowledge because
the fear of losing their privileged position, ownership, authority, and superiority.
Successful incentive systems are an effective way to motivate individual, especially
those who are willing to share their knowledge, to facilitate knowledge sharing and
further improve business performance (Lee & Ahn, 2007). The emotional involvement
in FCBs may not a reason to motivate individual share knowledge, the findings is
contrary to Chirico and Nordqvist(2010) state, especially incentive systems need a fair
comments such as individual’s appraisal relate to rewards at their motivational levels
(Lacke, 1976).
The result is stronger in terms of statistical significance as compared to training for
workers hypothesis H2.1. FCBs have a stronger moderating effect in H2.2such that
LEE, Yuk Ling Angie Student Number: C3173954
132
lower values of FCBs will increase the strength of the relationship between incentive
systems and knowledge sharing.
.
H2.3: In the HKCI, FCBs act as a moderating factor in the relationship between trust
and knowledge sharing.
This final hypothesis (H2.3) proposes that the relationship between trust and
knowledge sharing is moderated by FCBs in the HKCI. The interaction (AT_AQ) effects
were entered in Model 2 and the result indicates that when the interaction term was
added.
The result is the strongest in terms of statistical significance as compared to the other
two Hypotheses (H2.1 and H2.2). FCBs also have a moderating effect in H2.3, which
supports a significant relationship between trust and knowledge sharing, such that
lower values of FCBs will increase the strength of the relationship between trust and
knowledge sharing. This finding is consistent with prior results (Zahra, 2010; Zahra et
al., 2007). The present results indicate that lower involvement of FCBs will have a
stronger moderating effects between trust and knowledge-sharing behaviour. This may
be caused by lower involvement of FCBs, which may potentially inhibit such exchanges
(Zahra & Nielsen, 2002) through paternalistic values (Chirico and Nordqvist, 2010) and
centralization of power through the boss (Redding, 1979, 1984), the most valuable
information resides in a few closely associated family members and resistant to change
usually (Hall et al., 2001).
5.6 Theoretical implications
Scholars and practitioners interested in FCBs studies seek to gain new insights and
knowledge into the causal processes that underlie these firms (Lewin, 1940). The
LEE, Yuk Ling Angie Student Number: C3173954
133
current study contributes to the understanding of knowledge sharing by identifying
several theoretical implications.
First, to the best of the student researcher’s knowledge, this study may be one of the
first attempts to examine the moderating role of FCBs on the relationship among AMO
factors and knowledge sharing, especially in the context of the HKCI. This study offers
important findings that identify the importance of the AMO model in knowledge sharing
and the moderating role of FCBs in HKCI. This is also vital in the creation of a conceptual
framework to stimulate understanding and guide the examination of knowledge
sharing, while also encouraging further research in this field.
Second, this study examines the antecedents of knowledge sharing in FCBs in Hong
Kong in a way that is consistent with the study of Zahra et al. (2006). Applying the AMO
model allows for evaluating the impact of individual AMO factors on knowledge sharing
through the moderating effects of FCBs. Empirical evidence demonstrates statistically
significant positive relationships AMO factors have with knowledge sharing behaviours.
Third, the AMO model is a widely accepted model in studies of high performance
through HRM practices in previous studies (Appelbaum et al., 2000; Sergio & Williams,
2008; Nisha and Wickramasinghe, 2016). For the current study, a particularly helpful
aspect of the AMO model is that it assumes the presence of all factors in influencing
knowledge sharing with FCBs for inducing a moderating effect. Based on the results,
AMO can be an important model for explaining the knowledge-sharing behaviour in
firms (Bock & Kim, 2001). What follows from the findings is that if a set of high HRM
practices are implemented as a bundle, then this is likely to explain the presence of
knowledge sharing behaviours.
Fourth, using the Process Macro analysis in SPSS shows that the moderating effect of
FCBs for each of the AMO factors of training for workers (marginal statistical
significance), compared to other factors of incentive systems and trust. Trust has the
LEE, Yuk Ling Angie Student Number: C3173954
134
strongest effect (higher than training for workers and incentive systems). This finding
gives rise to several management implications.
Finally, this study formulated a conceptual framework and tested theories from
knowledge-sharing and AMO factors. This study can form the basis for researchers to
conduct additional research in different industry contexts as well as considering a wider
range of AMO practices that have been identified in the HRM high-performance work
systems stream to advance scholarship in this field.
5.7 Managerial implications
The findings of this study raises several implications for practice. First, managers in the
HKCI should offer training for workers to improve their understanding of knowledge
management processes, marketing, competition and technological trends, and team
building, to realise better knowledge-sharing outcomes (Cabrera & Cabrera, 2005; Lin,
2007; Zahra et al., 2007). Thus, investing in training for workers is critical for future
success of FCBs and sustaining competitive advantages in rapidly their changing market
(Zahra, 2005). Measures, such as ISO900, to guide the implementation of standards in
training programs are needed.
Training programs may also provide incentives for enhancing team spirit, which, in turn,
can improve a firm’s positive culture and boost its performance (Fox & Guyer, 1979;
Kahan, 1973: Shih et al., 2006). Training programs such as team building, cross-training,
and harnessing technological developments, can increase the levels of cognitive,
structural, and relational social capital as well as stimulate knowledge-sharing
behaviours (Cabrera & Cabrera,2005; Kang et al., 2003). Different reports can be
generated for the purposes of management and marketing analyses.
LEE, Yuk Ling Angie Student Number: C3173954
135
To build relationships, team-based trainings are important for the transfer of
knowledge (Fleishman, 1980; Axelrod, 1984 Van Lange et al., 1992; Cabrera & Cabrera,
2002). Cross-training also increases the interactions and allows for a common language
to enhance such interactions (Kramer & Brewer, 1984; Schneider, 1992; Cabrera &
Cabrera, 2002; Cabrera & Cabrera, 2005). Business managers not only seek to improve
business performance, but they also maintain a useful knowledge base for securing
future growth (Singh, 2008). Training to help people use the systems more efficiently
and for further reducing costs can be helpful (Cabrera & Cabrera, 2002).
Second, the implementation of incentive systems has been identified as influential
factor of the AMO model. Rewarding and recognizing these knowledge-sharing
behaviours sends a strong signal to the employees that the organization values
knowledge-sharing in FCBs (Cabrera & Cabrera, 2005). An incentive system,
incorporating extrinsic (e.g. Money, avoidance of punishment etc.) and intrinsic (praise)
rewards, (Foss et al., 2009), can motivate people to practice reciprocal behaviours of
knowledge sharing (Fehr & Fischbacher, 2002). Reciprocal behaviour is important,
because it affects the fundamental methods in the functioning of markets, firms,
incentives, and collective actions (Fehr & Fischbacher, 2002). However, some
researchers found that incentive systems may reduce any corresponding increases in
efforts as it can create a hostile atmosphere and even induce negative reciprocity
(Bewley, 1999; Fehr & Fischbacher, 2002).
Third, trust has been identified as an influential factor in fostering knowledge sharing
behaviours. Thus, it is critical for managers to improve and develop the strategy of
team building. Long-term relationships between managers and peers are needed to
improve the knowledge-sharing performance within firms. Trust can be improved by
team-building strategies to enhance employees’ willingness to share knowledge.
Employees may, through knowledge sharing, reduce the supervisor’s expert power
through team spirit and create a knowledge-sharing atmosphere in a firm. Trust can
also improve the efficiency of knowledge exchange and mutual understanding among
LEE, Yuk Ling Angie Student Number: C3173954
136
peers, managers and staff, as proven by results between trust and knowledge sharing
(Abrams et al., 2003; Moravian et al., 2006), especially in FCBs.
Furthermore, Others have argued that trust is the least costly and the most effective
method to encourage people to share their knowledge (Dyer & Singh, 1998; Sharratt
& Usoro, 2003). For this reason, trust treats as a solution to motivate employee to
share knowledge. When individual views a firm as enhancing trustworthy values, such
as honesty, reliability and
mutual reciprocity, the commitment seems to be a greater standard of motivation to
share individual knowledge within that firm (Sharratt & Usoro, 2003). Thus, high levels
of interpersonal trust relates to high levels of willingness to share knowledge
(Kalantzis & Cope, 2003). If employees work in a trusting environment, wherein a firm
recognizes and values their contributions and where they can count on reciprocity,
then they become naturally more willing to share their knowledge (Cabrera & Cabrera,
2005). Thus, for fostering knowledge sharing, organizations must create a trusting
environment.
Some researchers have demonstrated that FCBs may have too much personalized
control. For example, Hong Kong partners may not be willing to share their
management and marketing knowledge in the HKCI, which may impact the growth of
the latter (Merck & Yeung, 2003). Therefore, the management of FCBs may require
appointment of Non-FCB members into the board of directors of FCBs. Such a move
may send a signal to the employees that high abilities of the employees are highly
respected and needed in FCBs.
5.8 Contributions
The major contributions of this study can be summarized as follows. First, knowledge
sharing is driven by the AMO factors. Second, the study fills the gap in the literature
LEE, Yuk Ling Angie Student Number: C3173954
137
concerning the role of FCBs as a moderating factor affecting the relationship of the AMO
model and knowledge sharing. Third, the study highlights the role of managers in FCBs
in relation to the promotion of the AMO factors and enhancement of the competitive
advantages by sustaining long-term improvements. Firms may provide training for
workers to encourage the development of knowledge sharing because it is an important
determinant of the intention to share knowledge. The development of an affective trust
can also be nurtured during such trainings, which will encourage employees to
consistently demonstrate a genuine concern for their colleagues and act in ways that is
in the best interests of their colleagues. One way to encourage managers and peers to
act in this way is to provide them with an environment that fosters trust and friendly
cooperation, and in which rewards are collective rather than individualistic. For
example, incentive systems to reward group success may motivate knowledge sharing
and foster team spirit within a firm.
Finally, the distinct characteristics of FCBs is to facilitate knowledge sharing with a
strong sense of identity (Lansberg, 1999). Knowledge sharing occurs when people who
share a common purpose, experience similar problems come together to exchange
ideas. Although knowledge is very important, FCBs may have several characteristics that
can potentially inhibit such exchanges. In addition, family members may not have the
same levels of entrepreneurial spirt (Merck & Yeung, 2003). Family rivalries may just
limit some senior members to share knowledge with the next generation. In fact, some
of next generation managers may not want to learn at all. These rivalries are common
and happen in family members or non-family members in FCBs (Grote, 2003). This
finding is consistent with the findings of past studies (Zahra, 2007; Gomez–Mejia et al.,
2001).
5.9 Limitations and future research
LEE, Yuk Ling Angie Student Number: C3173954
138
The first limitation pertains to the research context, as all data were collected in Hong
Kong. Trading behaviours in Hong Kong may not be generalizable to other Chinese
business communities. Other countries, such as Singapore and Taiwan, may be further
considered in future research focused on firms within the Asia-Pacific Region.
The second limitation relates to the research context. The adopted questionnaire for
this study was distributed online, using a convenience sampling approach to collect data
from the HKCI firms, targeting participants, such as CEOs, top management, and senior
managers. With this profile, it may be difficult to obtain good response rates.
Furthermore, convenient sampling is problematic in that it may not be representative
of the entire population being examined.
Third, the quantitative survey method measures the strengths of the statistical
relationships analysed and may not provide a conclusive direction of the cause and
effects involved. In addition, external factors, such as staff turnover, benefits and salary
packages, as well as market needs are significant factors that influence training for
workers, incentive systems, and trust (Baker et al., 1988; Batt, 2002). Hence, this study
may be unable to comment on the effect of factors that were not considered.
Fourth, the design of this study uses small a sample size that may be unable to
sufficiently support the results. This also creates high multicollinearity in all three
interactions. Nevertheless, this study used the Process Macro in SPSS is a tool to
bootstrap the sample from 100 to 1000 units, and it helps solve the high
multicollinearity problem encountered in a multiple regression analysis (Preacher &
Hayes, 2008; Nimon et al., 2010).
Fifth, the data were obtained from a single source (i.e., managers or top management)
and a single method (i.e., Likert scale-based questionnaire). Therefore, common-
LEE, Yuk Ling Angie Student Number: C3173954
139
method biases may be present, because respondents might have responded similarly
on all scales given the similarity of their format (Cook & Campbell, 1983).
Meanwhile, several suggestions for future research are also offered.
First, this study is the first attempt to adapt the framework of Zahra et al. (2007) to test
the knowledge sharing as moderated by FCBs in relation to AMO factors for improving
knowledge-sharing within the HKCI. This study can be adopted to further explore how
the AMO model works between formal and informal knowledge sharing outcomes
individually. Furthermore, the conceptual model should be examined using different
industries and cultural groups, because contextual factors may influence the
hypothesized relationships.
Second, qualitative research methods can also be used to investigate the antecedents
of intention to share explicit and implicit knowledge. Qualitative research methods
might be particularly valuable when examining the antecedents of sharing informal
knowledge, because articulating and measuring informal knowledge using quantitative
methods is more difficult than doing the same for formal knowledge.
Third, control variables, such as role competence, can also be used in future studies,
because sharing knowledge does not always depend on an individual’s willingness to
share.
Fourth, future research can employ a longitudinal approach to test the conceptual
model and obtain a better understanding of the hypothesized research’s causal
mechanisms.
Finally, from a methodological and analytical perspective, smart PLS can be used for
undertaking variance-based SEM and increasing the amount of information in the
original data, because such an approach reduces the effect of random sampling errors
via bootstrapping procedures (Hair, 2016; Peng & Lai, 2012).
LEE, Yuk Ling Angie Student Number: C3173954
140
5.10 Summary and concluding remarks
Refer to the table 5.11 Result and Discussion of Hypotheses testing Findings, this study
explored the moderating role of FCBs in knowledge sharing and fills a gap in the
international literature by exploring its relationship with AMO factors in the context of
HKCI firms. The study has theoretical and managerial implications.
Table 5.11 Result and Discussion of Hypotheses Testing Findings
Research questions Related Hypotheses Result and Discussion
Q1 Hypothesis 1.1 (H1.1) Results: Sig (.75) Marginal supported
Does ability ( training workers), motivation ( providing incentive systems), opportunity (creating an environment of trust) of employees have a significant effect on knowledge sharing in the HK clothing industry (HKCI)?
In the HKCI, Training for Workers is positively associated with Knowledge Sharing.
The result is marginally significant for training for workers and has a moderate and prositive influence on knowledge sharing. Training in inter-personal communication skills may help employees to exchange information and knowledge effectively. (Aragón-Sánchez, 2003; Cabrera & Cabrera, 2005; Wong & Aspinwall, 2005),
Hypothesis 1.2 (H1.2) Results: Sig (.35) Supported
In the HKCI, Incentive Systems is positively associated with Knowledge Sharing.
The result supported incentive systems as a predictor of knowledge sharing performance. Therefore, in designing incentive and rewards systems it is vital firms should provide new intrinsic opportunities that allow one to learn and actualize their full pontential. ( Sharratt & Usoro, 2003; Wong & Aspinwall, 2005).
Hypothesis 1.3 (H1.3) Results:Sig (.000) Strongly Supported
In the HKCI, Trust is positively associated with Knowledge Sharing.
The result is the strongest in relation to trust and it is the most effecitve and often the least costly method to encourage people to share their knowledge. However, building trust requires sincere efforts by a firm’s leaders (Dyer & Singh, 1998; Sharratt & Usoro, 2003). Overall the first major set of findings from H1.1 , H1.2, H1.3 answers the first research question that AMO factors positively influence knowledge sharing in HKCI.
LEE, Yuk Ling Angie Student Number: C3173954
141
Q2 Hypothesis 2.1(H2.1) Results:Sig (.04) Supported
What are the key relationships between FCBs , AMO factors and knowledge sharing in the HKCI firms?
In the HKCI, FCBs acts as a moderating factor in the relationship between Ability ( Training for Workers) and Knowledge Sharing.
The result supported the assumption that FCBs moderate the impact of kowledge-sharing behavior when training for workers is provided . Lower values of FCBs will increase the strength of the relationship between training for workers and knowledge sharing (Kotey, 2007 and Aragon-sancher,2003).
Hypothesis 2.2( H2.2) Results:Sig (.01) Supported
In the HKCI, FCBs acts as a moderating factor in the relationship between Motivation ( Incentive Systems) and Knowledge Sharing.
The result identifed that FCBs have a stronger moderating effect on the relationship. Lower values of FCBs will increase the strength of the relationship between incentive systems and knowledge sharing.
Hypothesis 2.3 (H2.3) : Results:Sig (.00) Strongly Supported
In the HKCI, FCBs acts as a moderating factor in the relationship in between Opportunity ( trust) and knowledge sharing
The result is the strongest to support FCBs moderating effect. Lower involvement of FCBs will increase the strength of the relationship between trust and knowledge sharing (Zahra, 2010; Zahra et al., 2007). The second major finding answered Q2 that paternalism and personalized culture in FCBs can create a neagtive impact o knowledge sharing.
For practical managerial implications, this research is timely study as some junior family
members of business clans may have no ambition to seek new knowledge or for growing
the business as they may simply lack interest in the family business (Le Breton-Miller et
al., 2004). Changing business environments in this major manufacturing sector in Hong
Kong are forcing family-owned clothing industry firms to look for suitable strategies to
improve their firms’ competitive advantages. Creating and managing unique knowledge
is important in sustaining a firm’s competitive advantage over others (Barney 1991;
Lank, 1997). Consequently, by considering the ownership type (FCBs) in the analysis,
acknowledging the need towards a shared understanding of the HKCI firms may help
such firms develop their capability for business success in the future, especially in view
of shifts in intergenerational leadership.
Business performance can be enhanced through knowledge sharing. A firm’s
performance in today’s dynamic environment and its sustainable competitive
LEE, Yuk Ling Angie Student Number: C3173954
142
advantages relies on its ability to fully equip its knowledge-management processes to
its business needs.
The application of AMO factors is likely to foster knowledge sharing via establishing an
organizational environment that is contributory to sharing; knowledge sharing can be
encouraged by building positive attitudes against sharing and enhancing perceptions
and norms towards sharing. Current HRM literature argues that firms should have a
strategic orientation towards acquiring and sharing knowledge within and across
organisations (Wright et al., 1994) that endorse it to build firm-specific human capital
for developing a sustainable competitive advantage.
The above implies that firms can be succeed with various strategic is subject to various
types of human capital... However, in a dynamic and-changing competitive
environment, one key capability that is applied for effective regardless of a firm’s
strategy: is the ability to continuously to renew its knowledge base. HRM practices
should therefore, dedicate greater efforts to enhance the creation, acquisition and flow
of knowledge through knowledge sharing for creating an adaptive organisation.
To this end, future scholarship should focus on developing an understanding of
knowledge-sharing by AMO model and leadership execute that will facilitate the
exchange of knowledge culture in firms.
LEE, Yuk Ling Angie Student Number: C3173954
143
References
Abelson, R. P. (1985). A variance explanation paradox: When a little is a lot.
Psychological Bulletin, 97(1), 129.
Adler, P. S., & Kwon, S. W. (2002). Social capital: Prospects for a new concept.
Academy of management review, 27(1), 17-40.
Aguinis, H., O'Boyle, E., Gonzalez‐Mulé, E., & Joo, H. (2016). Cumulative
Advantage: Conductors and Insulators of Heavy‐Tailed Productivity
Distributions and Productivity Stars. Personnel Psychology, 69(1), 3-66.
Aragon-Correa, J. A., & Sharma, S. (2003). A contingent resource-based view
of proactive corporate environmental strategy. Academy of management
review, 28(1), 71-88.
Aragón-Sánchez, A., Barba-Aragón, I., & Sanz-Valle, R. (2003). Effects of
training on business results, The International Journal of Human Resource
Management, 14(6), 956-980.
Ardichvili, A., Page, V., & Wentling, T. (2003). Motivation and barriers to
participation in virtual knowledge-sharing communities of practice. Journal of
knowledge management, 7(1), 64-77. Argot, L. (1999). Organizational
learning.
Argote, L., McEvily, B., & Reagans, R. (2003). Managing knowledge in
organizations: An integrative framework and review of emerging themes.
Management science, 49(4), 571-582.
LEE, Yuk Ling Angie Student Number: C3173954
144
Arnold, H. J., & Evans, M. G. (1979). Testing multiplicative models does not
require ratio scales. Organizational Behavior and Human Performance, 24(1),
41-59.
Alam, S. S., Abdullah, Z., Ishak, N. A., & Zain, Z. M. (2009). Assessing
knowledge sharing behaviour among employees in SMEs: An empirical study.
International Business Research, 2(2), 115.
Alavi, M., Kayworth, T. R., & Leidner, D. E. (2005). An empirical examination of
the influence of organizational culture on knowledge management practices.
Journal of management information systems, 22(3), 191-224.
Alavi, M., & Leidner, D. E. (2001). Review: knowledge management and
knowledge management systems: conceptual foundations and research
issues. MIS Quarterly, 25(1), pp. 107-136.
Aiken, L. S., West, S. G., & Reno, R. R. (1991). Multiple regression: Testing and
interpreting interactions. Sage.
Allen, J., James, A. D., & Gamlen, P. (2007). Formal versus informal knowledge
networks in R&D: a case study using social network analysis. R&D
Management, 37(3), 179-196.
Aman, F. (2010). Organisational factors enhancing the use of information
technology for knowledge management: a study in Malaysian listed
organisations. Curtin University of Technology.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in
practice: A review and recommended two-step approach. Psychological
bulletin, 103(3), 411.
LEE, Yuk Ling Angie Student Number: C3173954
145
Appelbaum, E. (2000). Manufacturing advantage: Why high-performance
work systems pay off. Cornell University Press.
Appelbaum, E., Bailey, T., Berg, P., & Kalleberg, A. (2000). Manufacturing
competitive advantage: The effects of high performance work systems on
plant performance and company outcomes
Appelbaum, E., Bailey, T., Berg, P., & Kalleberg, A. (2000). Manufacturing
competitive advantage: The effects of high performance work systems on
plant performance and company outcomes.
Appelbaum, S. H., & Gallagher, J. (2000). The competitive advantage of
organizational learning. Journal of Workplace Learning, 12(2), 40-56.
Apshvalka, D., & Wendorff, P. (2005, September). A Framework of Personal
Knowledge Management in the Context of Organisational Knowledge
Management. In ECKM (pp. 34-41).
Au, K., Peng, M. W., & Wang, D. (2000). Interlocking directorates, firm
strategies, and performance in Hong Kong: Towards a research agenda. Asia
Pacific, Journal of Management, 17(1), 29-47.
Au, K., Chiang, F. F., Birtch, T. A., & Ding, Z. (2013). Incubating the next
generation to venture: The case of a family business in Hong Kong. Asia Pacific
Journal of Management, 30(3), 749-767.
Augier, M., Shariq, S. Z., & Thanning Vendelø, M. (2001). Understanding
context: Its emergence, transformation and role in tacit knowledge sharing.
Journal of knowledge management, 5(2), 125-137.
LEE, Yuk Ling Angie Student Number: C3173954
146
Axelrod, R. M. (2006). The evolution of cooperation. Basic books.
Aydin, S., Ö zer, G., & Arasil, Ö . (2005). Customer loyalty and the effect of
switching costs as a moderator variable: A case in the Turkish mobile phone
market. Marketing Intelligence & Planning, 23(1), 89-103.
Bagozzi, R. P. (Ed.). (1994). Principles of marketing research (pp. 386-422).
Cambridge, MA: Blackwell.
Bailey, T. (1993). Organizational innovation in the apparel industry. Industrial
Relations: A Journal of Economy and Society, 32(1), 30-48.
Becker, G. S. (1964). Human Capita.
Baker, G. P., Jensen, M. C., & Murphy, K. J. (1988). Compensation and
incentives: Practice vs. theory. The journal of Finance, 43(3), 593-616.
Baker, M. (2002a) Research methods, The Marketing Review, 3, 2, pp.167-
193.
Bartlett MS. Tests of significance in factor analysis. British Journal of
Psychology. 1950;3(Part II):77-85.
Barkema, H. G., Chen, X. P., George, G., Luo, Y., & Tsui, A. S. (2015). West
meets East: New concepts and theories. Academy of Management Journal,
58(2), 460-479.
Barlett, J. E., Kotrlik, J. W., & Higgins, C. C. (2001). Organizational research:
Determining appropriate sample size in survey research. Information
technology, learning, and performance journal, 19(1), 43.
LEE, Yuk Ling Angie Student Number: C3173954
147
Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable
distinction in social psychological research: Conceptual, strategic, and
statistical considerations. Journal of personality and social psychology, 51(6),
1173.
Bartol, K. M., & Srivastava, A. (2002). Encouraging knowledge sharing: The
role of organizational reward systems. Journal of Leadership & Organizational
Studies, 9(1), 64-76.
Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Wiley.
Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2014). Hierarchical modeling and
analysis for spatial data. Crc Press.
Bartlett MS. Tests of significance in factor analysis. British Journal of
Psychology.1950; 3(Part II):77-85.
Barney, J. (1991) Firm resources and sustained competitive advantage, Journal
of Management, 17, 1, pp. 99-120.
Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable
distinction in social psychological research: Conceptual, strategic, and
statistical considerations. Journal of personality and social psychology, 51(6),
1173.
Barlett, J. E., Kotrlik, J. W., & Higgins, C. C. (2001). Organizational research:
Determiningappropriate sample size in survey research. Information
technology, learning, and performance journal, 19(1), 43.
LEE, Yuk Ling Angie Student Number: C3173954
148
Bartol, K. M., & Srivastava, A. (2002). Encouraging knowledge sharing: The
role of organizational reward systems. Journal of Leadership & Organizational
Studies, 9(1), 64-76.
Barkema, H. G., Chen, X. P., George, G., Luo, Y., & Tsui, A. S. (2015). West
meets East: New concepts and theories. Academy of Management Journal,
58(2), 460-479.
Batt, R. (2002). Managing customer services: Human resource practices, quit
rates, and sales growth. Academy of management Journal, 45(3), 587-597.
Becker, B. E., Huselid, M. A., Becker, B. E., & Huselid, M. A. (1998). High
performance work systems and firm performance: A synthesis of research and
managerial implications. In Research in personnel and human resource
management.
Belanger, F., Hiller, J. S., & Smith, W. J. (2002). Trustworthiness in electronic
commerce: the role of privacy, security, and site attributes. The journal of
strategic Information Systems, 11(3), 245-270.
Belsley, D. A., Kuh, E., & Welsch, R. E. (1980). Detecting and assessing
collinearity. Regression diagnostics: Identifying influential data and sources of
collinearity, 85-191.
Bharadwaj, A. S. (2000). A resource-based perspective on information
technology capability and firm performance: an empirical investigation. MIS
quarterly, 169-196.
LEE, Yuk Ling Angie Student Number: C3173954
149
Bhatt, G. D. (2001). Knowledge management in organizations: examining the
interaction between technologies, techniques, and people. Journal of
knowledge management, 5(1), 68-75.
Black, J. A., & Boal, K. B. (1994). Strategic resources: Traits, configurations and
paths to sustainable competitive advantage. Strategic management journal,
15(S2), 131-148.
Blumberg, M., & Pringle, C. D. (1982). The missing opportunity in
organizational research: Some implications for a theory of work performance.
Academy of management Review, 7(4), 560-569.
Bock, G. W., & Kim, Y. G. (2001). Breaking the myths of rewards: An
exploratory study of attitudes about knowledge sharing. Pacis 2001
proceedings, 78.
Bock, G. W., Zmud, R. W., Kim, Y. G., & Lee, J. N. (2005). Behavioral intention
formation in knowledge sharing: Examining the roles of extrinsic motivators,
social-psychological forces, and organizational climate. MIS quarterly, 87-111.
Bogdan, R. C., & Biklen, S. K. (1998). Foundations of qualitative research in
education. Qualitative research in education: An introduction to theory and
methods, 1-48.
Bogdanowicz, M. S., & Bailey, E. K. (2002). The value of knowledge and the
values of the new knowledge worker: generation X in the new economy.
Journal of European Industrial Training, 26(2/3/4), 125-129.
Bollinger, A. S., & Smith, R. D. (2001). Managing organizational knowledge as a
strategic asset. Journal of knowledge management, 5(1), 8-18.
LEE, Yuk Ling Angie Student Number: C3173954
150
Boselie, P. (2010). High performance work practices in the health care sector:
a Dutch case study. International Journal of Manpower, 31(1), 42-58.
Boselie, P., & Veld, M. (2012). Human resource management and
commitment in Dutch child day care. Administration in Social Work, 36(2),
189-211.
Bos‐Nehles, A. C., Van Riemsdijk, M. J., & Kees Looise, J. (2013). Employee
perceptions of line management performance: applying the AMO theory to
explain the effectiveness of line managers' HRM implementation. Human
resource management, 52(6), 861-877.
Breton‐Miller, L., & Miller, D. (2006). Why do some family businesses out‐
compete? Governance, long‐term orientations, and sustainable capability.
Entrepreneurship theory and practice, 30(6), 731-746.
Bogdanowicz, M. S., & Bailey, E. K. (2002). The value of knowledge and the
values of the new knowledge worker: generation X in the new economy.
Journal of European Industrial Training, 26(2/3/4), 125-129.
Bollinger, A. S., & Smith, R. D. (2001). Managing organizational knowledge as a
strategic asset. Journal of knowledge management, 5(1), 8-18.
Boselie, P. (2010). High performance work practices in the health care sector:
a Dutch case study. International Journal of Manpower, 31(1), 42-58.
Boxall, Peter, and Keith Macky. "Research and theory on high‐performance
work systems: progressing the high‐involvement stream." Human Resource
Management Journal, 19.1 (2009): 3-23.
LEE, Yuk Ling Angie Student Number: C3173954
151
Bontis, N. (1998). Intellectual capital: an exploratory study that develops
measures and models. Management decision, 36(2), 63-76.
Bos‐Nehles, A. C., Van Riemsdijk, M. J., & Kees Looise, J. (2013). Employee
perceptions of line management performance: applying the AMO theory to
explain the effectiveness of line managers' HRM implementation. Human
resource management, 52(6), 861-877.
Breton‐Miller, L., & Miller, D. (2006). Why do some family businesses out‐
compete? Governance, long‐term orientations, and sustainable capability.
Entrepreneurship theory and practice, 30(6), 731-746.
Biometrika Sheer, V. C. (2010). Transformational and Paternalistic Leaderships
in Chinese Organizations: Construct, Predictive, and Ecological Validities
Compared in a Hong Kong Sample. Intercultural Communication Studies,
19(1), 121-140.
Boselie, P., Paauwe, J., & Richardson, R. (2003). Human resource
management, institutionalization and organizational performance: a
comparison of hospitals, hotels and local government. The International
Journal of Human Resource Management, 14(8), 1407-1429.
Bowen, D. E., & Lawler, E. E. (1995). Empowering service employees. Sloan
management review, 36(4), 73.
Boudreau, J. W., & Ramstad, P. M. (2002). Strategic HRM measurement in the
21st century: From justifying HR to strategic talent leadership.
Boxall, P. Purcell, j.(2003) strategy and human resource management.
LEE, Yuk Ling Angie Student Number: C3173954
152
Boxall, Peter, and Keith Macky (2009). "Research and theory on high‐
performance work systems: progressing the high‐involvement stream."
Human Resource Management Journal 19.1: 3-23.
Buckley, P. J., & Strange, R. (2011). The governance of the multinational
enterprise: Insights from internalization theory. Journal of Management
Studies,48(2), 460-470.
Buckman, R. H. (1998). Knowledge sharing at Buckman labs. Journal of
Business Strategy, 19(1), 11-15.
Burrows, G. R., Drummond, D. L., & Martinsons, M. G. (2005). Knowledge
management in China. Communications of the ACM, 48(4), 73-76.
Cabrera, A., & Cabrera, E. F. (2002). Knowledge-sharing dilemmas.
Organization studies, 23(5), 687-710.
Cabrera, E. F., & Cabrera, A. (2005). Fostering knowledge sharing through
people management practices. The international journal of human resource
management, 16(5), 720-735
Cabrera, A., Collins, W. C., & Salgado, J. F. (2006). Determinants of individual
engagement in knowledge sharing. The International Journal of Human
Resource Management, 17(2), 245-264
Cabrera-Suárez, K., De Saá-Pérez, P., & García-Almeida, D. (2001). The
succession process from a resource-and knowledge-based view of the family
firm. Family Business Review, 14(1), 37-46.
LEE, Yuk Ling Angie Student Number: C3173954
153
Calof, J. L. (1994). The relationship between firm size and export behavior
revisited. Journal of International Business Studies, 25(2), 367-387.
Campbell, C. A., Selles, F., Zentner, R. P., & McConkey, B. G. (1993). Available
water and nitrogen effects on yield components and grain nitrogen of zero-till
spring wheat. Agronomy Journal, 85(1), 114-120.
Carney, M. (2005). Corporate governance and competitive advantage in
family‐controlled firms. Entrepreneurship theory and practice, 29(3), 249-265.
Cascio, W. F. (1986). Managing human resources. New york: McGraw-Hill.
Casimir, G., Lee, K., & Loon, M. (2012). Knowledge sharing: influences of trust,
commitment and cost. Journal of Knowledge Management, 16(5), 740-753.
Chai, S., & Kim, M. (2010). What makes bloggers share knowledge? An
investigation on the role of trust. International Journal of Information
Management, 30(5), 408-415.
Chang, Y. Y., Gong, Y., & Peng, M. W. (2012). Expatriate knowledge transfer,
subsidiary absorptive capacity, and subsidiary performance. Academy of
Management Journal, 55(4), 927-948.
Champoux, J. E., & Peters, W. S. (1987). Form, effect size and power in
moderated regression analysis. Journal of Occupational Psychology, 60(3),
243-255.
Chrisman, J. J., Chua, J. H., Kellermanns, F. W., & Chang, E. P. (2007). Are
family managers agents or stewards? An exploratory study in privately held
family firms. Journal of Business research, 60(10), 1030-1038.
LEE, Yuk Ling Angie Student Number: C3173954
154
Chua, J., Chrisman, J., & Chang, E. (2004). “Are Family Firms Born or Made? An
Exploratory Investigation”. Family Business Review, 17(1), pp. 37-55.
Chua, R. Y. J., Ingram, P., & Morris, M. W. (2008). From the head and the
heart: Locating cognition-and affect-based trust in managers' professional
networks. Academy of Management journal, 51(3), 436-452.
Chirico, F., & Nordqvist, M. (2010). Dynamic capabilities and trans-
generational value creation in family firms: The role of organizational culture.
International Small Business Journal, 28(5), 487-504.
Chiu, R. K., Wai-Mei Luk, V., & Li-Ping Tang, T. (2002). Retaining and
motivating employees: Compensation preferences in Hong Kong and China
Personnel Review, 31(4), 402-431.
Chirico, F., & Salvato, C. (2008). Knowledge integration and dynamic
organizational adaptation in family firms. Family Business Review, 21(2), 169-
181.
Chirico, F., & Nordqvist, M. (2010). Dynamic capabilities and trans-
generational
value creation in family firms: The role of organizational culture. International
Small Business Journal, 28(5), 487-504.
Choy Chong, S. (2006). KM critical success factors: a comparison of perceived
importance versus implementation in Malaysian ICT companies. The learning
organization, 13(3), 230-256.
LEE, Yuk Ling Angie Student Number: C3173954
155
Chrisman, J. J., Chua, J. H., & Sharma, P. (2005). Trends and directions in the
Development of a strategic management theory of the family firm.
Entrepreneurship theory and practice, 29(5), 555-576.
Chrisman, J. J., Steier, L. P., & Chua, J. H. (2006). Personalism, particularism,
and the competitive behaviors and advantages of family firms: An
introduction. Entrepreneurship Theory and Practice, 30(6), 719-729.
Chua, J., Chrisman, J., & Chang, E. (2004). “Are Family Firms Born or Made? An
Exploratory Investigation”. Family Business Review, 17(1), pp. 37-55.
Chiu, C. M., Hsu, M. H., & Wang, E. T. (2006). Understanding knowledge
sharing in virtual communities: An integration of social capital and social
cognitive theories. Decision support systems, 42(3), 1872-1888.
Chua, J., Chrisman, J., & Chang, E. (2004). “Are Family Firms Born or Made? An
Exploratory Investigation”. Family Business Review, 17(1), pp. 37-55.
Chung, Y. M., & Lee, J. Y. (2001). A corpus‐based approach to comparative
evaluation of statistical term association measures. Journal of the American
Society for Information Science and Technology, 52(4), 283-296.
Chuang, S. H. (2004). A resource-based perspective on knowledge
management capability and competitive advantage: an empirical
investigation. Expert systems with applications, 27(3), 459-465.
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (1983). Applied multiple
regression/correlation for the behavioral sciences.
LEE, Yuk Ling Angie Student Number: C3173954
156
Claessens, S., Djankov, S., Fan, J. P., & Lang, L. H. (2002). Disentangling the
incentive and entrenchment effects of large shareholdings. The journal of
finance, 57(6), 2741-2771.
Cohen, W. M., & Levinthal, D. A. (1989). Innovation and learning: the two
faces of R & D. The economic journal, 99(397), 569-596.
Combs, J., Liu, Y., Hall, A., & Ketchen, D. (2006). How much do high‐
performance work practices matter? A meta‐analysis of their effects on
organizational performance. Personnel psychology, 59(3), 501-528.
Cox, M. J. (2013). Formal to informal learning with IT: research challenges and
issues for e‐learning. Journal of computer assisted learning, 29(1), 85-105.
Cowley‐Durst, B. (1999). Gathering knowledge for your knowledge
management system. Performance Improvement, 38(7), 23-27.
Cropanzano, R., & Mitchell, M. S. (2005). Social exchange theory: An
interdisciplinary review. Journal of management, 31(6), 874-900.
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (1983). Applied multiple
regression/correlation for the behavioral sciences.
Collis, J., & Hussey, R. (2013). Business research: A practical guide for
undergraduate and postgraduate students. Palgrave macmillan.
Corti, L., Day, A., & Backhouse, G. (2000, December). Confidentiality and
informed consent: Issues for consideration in the preservation of and
LEE, Yuk Ling Angie Student Number: C3173954
157
provision of access to qualitative data archives. In Forum Qualitative
Sozialforschung/Forum: Qualitative Social Research (Vol. 1, No. 3).
Craig, J., & Dibrell, C. (2006). The natural environment, innovation, and firm
performance: A comparative study. Family Business Review, 19(4), 275-288.
Cronbach, L. J. (1987). Statistical tests for moderator variables: Flaws in
analyses recently proposed.
Currie, G., & Kerrin, M. (2003). Human resource management and knowledge
management: enhancing knowledge sharing in a pharmaceutical company.
The International Journal of Human Resource Management, 14(6), 1027-1045.
Cummings, L. L., & Schwab, D. P. (1973). Performance in organizations:
Determinants & appraisal. Good Year Books.
Davis, P. S., & Harveston, P. D. (1998). The influence of family on the family
business succession process: A multi-generational perspective.
Entrepreneurship theory and practice, 22, 31-54.
Diamantopoulos, A., Siguaw, J. A., & Siguaw, J. A. (2000). Introducing LISREL: A
guide for the uninitiated. Sage.
Dickerson, K. G. (1999). Textiles and apparel in the global economy. Prentice
Hall.
Dionne, S., Yammarino, F., Atwater, L., & Spangler, W. (2004).
Transformational leadership and team performance. Journal of Organizational
Change Management, 17, 177-193.
LEE, Yuk Ling Angie Student Number: C3173954
158
Davenport, T. H., & Prusak, L. (1998). Working knowledge: How organizations
manage what they know. Harvard Business Press.
Davison, R. M., Ou, C. X., & Martinsons, M. G. (2013). Information technology
to support informal knowledge sharing. Information Systems Journal, 23(1),
89-109.
Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and
new directions. Contemporary educational psychology, 25(1), 54-67.
Delery, J. E., & Shaw, J. D. (2001). The strategic management of people in
work organizations: Review, synthesis, and extension.
De Long, D. (1997). Building the knowledge-based organization: How culture
drives knowledge behaviors. Centers for Business Innovation–Working Paper.
De Vries, R. E., Van den Hoff, B., & de Ridder, J. A. (2006). Explaining
knowledge sharing, the role of team communication styles, job satisfaction,
and performance beliefs.
Communication Research, SAGE Publications
Dholakia, U. M., Basuroy, S., & Soltysinski, K. (2002). Auction or agent (or
both)? A study of moderators of the herding bias in digital auctions.
International Journal of Research in Marketing, 19(2), 115-130.
Dillman, D. A., Phelps, G., Tortora, R., Swift, K., Kohrell, J., Berck, J., & Messer,
B. L. (2009). Response rate and measurement differences in mixed-mode
surveys using mail, telephone, interactive voice response (IVR) and the
Internet. Social science research, 38(1), 1-18.
LEE, Yuk Ling Angie Student Number: C3173954
159
Ding, Z., Ng, F., & Cai, Q. (2007). Personal constructs affecting interpersonal
trust and willingness to share knowledge between architects in project design
teams. Construction Management and Economics, 25(9), 937-950.
Drucker, P. F. (1999). Management Challenges for the 21st Century. New York:
HarperCollins.
Drucker, P. F. (1999). Harvard business review on knowledge management,
Boston, MA: Harvard Business School Press.
Eddleston, K. A., & Kellermanns, F. W. (2007). Destructive and productive
family
relationships: A stewardship theory perspective. Journal of Business
Venturing, 22(4), 545-565.
Enright, M. J., Scott, E. E., & Dodwell, D. (1997). The Hong Kong Advantage.
OUP Catalogue.
Eisenhardt, K. M., & Santos, F. M. (2002). Knowledge-based view: A new
theory of strategy. Handbook of strategy and management, 1, 139-164.Ellis, K.
(2001), “Dare to share”, Training, February, pp. 74‐80
Evans, M. G. (1985). A Monte Carlo study of the effects of correlated method
variance in moderated multiple regression analysis. Organizational behavior
and human decision processes, 36(3), 305-323.
Fehr, E., & Fischbacher, U. (2002). Why social preferences matter–the impact
of non selfish motives on competition, cooperation and incentives. The
economic journal, 112(478), C1-C33.
LEE, Yuk Ling Angie Student Number: C3173954
160
Fernández, Z., & Nieto, M. J. (2005). Internationalization strategy of small and
medium‐sized family businesses: Some influential factors. Family Business
Review, 18(1), 77-89.
Firoz, F., & Chowdhury, M. F. (2015). Role of First Line Manager: Strategic
Leadership in Implementing Successful PMS. Asian Business Review, 2(1), 07-
12.
Ford, D. P., & Staples, S. (2010). Are full and partial knowledge sharing the
same? Journal of Knowledge Management, 14(3), 394-409.
Foss, N. J., Minbaeva, D. B., Pedersen, T., & Reinholt, M. (2009). Encouraging
knowledge sharing among employees: How job design matters. Human
resource management, 48(6), 871-893.
Fowler Jr, F. J. (1988). Survey research methods. Sage publications.
Fowler, A. (2000). The role of AI-based technology in support of the
knowledge management value activity cycle. The Journal of Strategic
Information Systems, 9(2), 107-128.
Fowler Jr, F. J. (2013). Survey research methods. Sage publications.
Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (1993). How to design and evaluate
research in education (Vol. 7). New York: McGraw-Hill.
LEE, Yuk Ling Angie Student Number: C3173954
161
Frazier, P. A., Tix, A. P., & Barron, K. E. (2004). Testing moderator and
mediator effects in counseling psychology research. Journal of counseling
psychology, 51(1), 115.
Ganesan, S., Malter, A. J., & Rindfleisch, A. (2005). Does distance still matter?
Geographic proximity and new product development. Journal of Marketing,
69(4), 44-60.
Garvin, D. (1993), “Building learning organizations”, Harvard Business Review,
July‐August, pp. 78‐91
Gliner, J. A., Morgan, G. A., & Leech, N. L. (2011). Research methods in applied
settings: An integrated approach to design and analysis. Routledge.
Gold, A. H., & Arvind Malhotra, A. H. S. (2001). Knowledge management: An
organizational capabilities perspective. Journal of management information
systems, 18(1), 185-214.
Gomez-Mejia, L. R., Larraza-Kintana, M., & Makri, M. (2003). The
determinants of executive compensation in family-controlled public
corporations. Academy of management journal, 46(2), 226-237.
Gorsuch RL. Factor Analysis. Hillsdale, NJ: Erlbaum; 1983.
Grimshaw, J. M., Santesso, N., Cumpston, M., Mayhew, A., & McGowan, J.
(2006). Knowledge for knowledge translation: the role of the Cochrane
Collaboration. Journal of Continuing Education in the Health Professions,
26(1), 55-62.
LEE, Yuk Ling Angie Student Number: C3173954
162
Goh, S. C., & Ryan, P. J. (2002, April). Learning capability, organization factors
and firm performance. In Third European conference on organizational
knowledge, learning and capabilities (pp. 5-6).
Goh, S. C., Elliott, C., & Quon, T. K. (2012). The relationship between learning
capability and organizational performance: a meta-analytic examination. The
learning organization, 19(2), 92-108.
Grant, R. M. (1996). Toward a knowledge‐based theory of the firm. Strategic
management journal, 17(S2), 109-122.
Grote, G. (2004). Uncertainty management at the core of system design.
Annual reviews in control, 28(2), 267-274.
Gomez-Mejia, L. R., Nunez-Nickel, M., & Gutierrez, I. (2001). The role of family
ties in agency contracts. Academy of management Journal, 44(1), 81-95.
Guest, D. E. (1997). Human resource management and performance: a review
and research agenda. International journal of human resource management,
8(3), 263-276.
Guest, D. E. (2011). Human resource management and performance: still
searching for some answers. Human Resource Management Journal, 21(1), 3-
13.
Gumbley, H. (1998). Knowledge Management. Work Study, 47(5), 175-177.
Gupta, A. K., & Govindarajan, V. (2006). Knowledge management's social
dimension: lessons from Nucor Steel.
LEE, Yuk Ling Angie Student Number: C3173954
163
Haldin-Herrgard, T. (2000). Difficulties in diffusion of tacit knowledge in
organizations. Journal of Intellectual capital, 1(4), 357-365.
Hall, Laura, and Derek Torrington. "Letting go or holding on‐the devolution of
operational personnel activities." Human Resource Management Journal 8.1
(1998): 41-55.
Hall, A., Melin, L., & Nordqvist, M. (2001). Entrepreneurship as radical change
in the family business: Exploring the role of cultural patterns. Family Business
Review, 14(3), 193-208.
Hart, P., & Larson, B. M. (2014). Communicating About Invasive Species: How
“Driver” and“Passenger” Models Influence Public Willingness to Take Action.
Conservation Letters, 7(6), 545-552.
Hair J, Anderson RE, Tatham RL, Black WC(1995). Multivariate data analysis.
4th ed. New Jersey: Prentice-Hall Inc.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006).
Multivariate data analysis 6th Edition. New Jersey: Pearson Education.
Hair, J. F. (2009). Multivariate data analysis.
Hair, J. F., Wolfinbarger, M. F., Ortinau, D. J., & Bush, R. P. (2008). Essentials of
marketing research. McGraw-Hill/Higher Education.
Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on
partial least squares structural equation modeling (PLS-SEM). Sage
Publications.
LEE, Yuk Ling Angie Student Number: C3173954
164
Hargadon, A. B. (1998). Firms as knowledge brokers: Lessons in pursuing
continuous innovation. California management review, 40(3), 209-227.
Harman, C., & Brelade, S. (2000). Knowledge Management and the Role of HR.
Financial Times/Prentice Hall
Hayes, A. F. (2009). Statistical methods for communication science. Routledge.
Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional
process analysis: A regression-based approach. Guilford Press
Heo, D., & Yoo, Y. (2002). Knowledge sharing in post merger integration.
Working Papers on Information Environments, Systems and Organizations,2.
Henderson, J. (2001). Heritage, identity and tourism in Hong Kong.
International Journal of Heritage Studies, 7(3), 219-235.
Hislop, D. (2003). Linking human resource management and knowledge
management via commitment: A review and research agenda. Employee
relations, 25(2), 182-202.
Hitt, M. A., Bierman, L., Uhlenbruck, K., & Shimizu, K. (2006). The importance
of resources in the internationalization of professional service firms: The
good, the bad, and the ugly. Academy of Management Journal, 49(6), 1137-
1157.
HKTDC, Hong Kong Trade Development Council (2016) Profiles of Hong Kong
major manufacturing industries, updated June 2, 2016, Accessed July 3, 2016.
LEE, Yuk Ling Angie Student Number: C3173954
165
http://hong-kong-economy-research.hktdc.com/business-news/article/Hong-
Kong-Industry-Profiles/Clothing-Industry-in-Hong-
Kong/hkip/en/1/1X000000/1X003DCL.htm
Hodgson, E., & Rose, R. L. (2005). Human metabolism and metabolic
interactions of deployment-related chemicals. Drug metabolism reviews,
37(1), 1-39.
Holste, J. S., & Fields, D. (2010). Trust and tacit knowledge sharing and use.
Journal of knowledge management, 14(1), 128-140.
Howell, J. M., & Avolio, B. J. (1993). Transformational leadership, transactional
leadership, locus of control, and support for innovation: Key predictors of
consolidated-business-unit performance. Journal of applied psychology, 78(6),
891
Hsu, M. H., Ju, T. L., Yen, C. H., & Chang, C. M. (2007). Knowledge sharing
behavior in virtual communities: The relationship between trust, self-efficacy,
and outcome expectations. International journal of human-computer studies,
65(2), 153-169.
Hsu, C. L., & Lin, J. C. C. (2008). Acceptance of blog usage: The roles of
technology acceptance, social influence and knowledge sharing motivation.
Information & management, 45(1), 65-74.
Hsu, I. C. (2008). Knowledge sharing practices as a facilitating factor for
improving organizational performance through human capital: A preliminary
test. Expert Systems with applications, 35(3), 1316-1326.
LEE, Yuk Ling Angie Student Number: C3173954
166
Horwitch, M., & Armacost, R. (2002). Helping knowledge management be all it
can be. Journal of Business Strategy, 23(3), 26-31.
Howell, J. M., & Avolio, B. J. (1993). Transformational leadership, transactional
leadership, locus of control, and support for innovation: Key predictors of
consolidated-business-unit performance. Journal of applied psychology, 78(6),
891.
Huang, Q., Davison, R. M., & Gu, J. (2008). Impact of personal and cultural
factors on knowledge sharing in China. Asia Pacific Journal of Management,
25(3), 451-471.
Hughes, J. (2007, January). The ability-motivation-opportunity framework for
behavior research in IS. In System Sciences, 2007. HICSS 2007. 40th Annual
Hawaii International Conference on (pp. 250a-250a). IEEE.
Huang, Q., Davison, R. M., & Gu, J. (2011). The impact of trust, guanxi
orientation and face on the intention of Chinese employees and managers to
engage in peer‐to‐peer tacit and explicit knowledge sharing. Information
Systems Journal, 21(6),557-577.
Huselid, M. A. (1995). The impact of human resource management practices
on turnover, productivity, and corporate financial performance. Academy of
management journal, 38(3), 635-672.
Hyland, T., & Matlay, H. (1998). Lifelong learning and the ‘New Deal’
vocationalism: Vocational Training Qualifications and the Small Business
Sector. British Journal of Educational Studies, 46(4), 399-414.
LEE, Yuk Ling Angie Student Number: C3173954
167
Ipe, M. (2003). Knowledge sharing in organizations: A conceptual framework.
Human Resource Development Review, 2(4), 337-359.
Iran (2007-12). Journal of Kululanga, G. K., & McCaffer, R. (2001). Measuring
knowledge management for construction organizations. Engineering,
construction and architectural management, 8(5/6), 346-354.
Ismail, M., & Ashmiza, N. (2012). Key determinants of research-knowledge
sharing in UK higher education institutions (Doctoral dissertation, University of
Portsmouth).
Issa, R. R., & Haddad, J. (2008). Perceptions of the impacts of organizational
culture and information technology on knowledge sharing in construction.
Construction Innovation, 8(3), 182-201.
Ismail Al-Alawi, A., Yousif Al-Marzooqi, N., & Fraidoon Mohammed, Y. (2007).
Organizational culture and knowledge sharing: critical success factors. Journal
of knowledge management, 11(2), 22-42.
James, L. R., & Brett, J. M. (1984). Mediators, moderators, and tests for
mediation. Journal of Applied Psychology, 69(2), 307.
Jashapara, A. (2004). Knowledge Management: An integrated approach.
London: Prentice hall.
Jarrar, Y. F. (2002). Knowledge management: learning for organizational
experience. Managerial Auditing Journal, 17(6), 322-328.
LEE, Yuk Ling Angie Student Number: C3173954
168
Jerez Gómez, P., Céspedes Lorente, J. J., & Valle Cabrera, R. (2004). Training
practices and organisational learning capability: Relationship and implications.
Journal of European industrial training, 28(2/3/4), 234-256.
Jiang, K., Lepak, D. P., Hu, J., & Baer, J. C. (2012). How does human resource
management influence organizational outcomes? A meta-analytic
investigation of mediating mechanisms. Academy of management
Journal,55(6), 1264-1294.
Jin, B. (2004). Apparel industry in East Asian newly industrialized countries:
Competitive advantage, challenge and implications. Journal of Fashion
Marketing and Management: An International Journal, 8(2), 230-244.
Ju Choi, C., Cheng, P., Hilton, B., & Russell, E. (2005). Knowledge governance.
Journal of Knowledge Management, 9(6), 67-75.
Kahan, J. P. (1973). Noninteraction in an anonymous three‐person prisoner's
dilemma game. Behavioral Science, 18(2), 124-127.
Kang, S. C., Morris, S. S., & Snell, S. A. (2003). Extending the human resource
architecture: relational archetypes and value creation.
Kankanhalli, A., Tan, B. C., & Wei, K. K. (2005). Contributing knowledge to
electronic
Kaplan, R. S., & Norton, D. P. (2001). The strategy-focused organization.
Strategy and Leadership, 29(3), 41-42.
LEE, Yuk Ling Angie Student Number: C3173954
169
Karatepe, O. M., & Kilic, H. (2007). Relationships of supervisor support and
conflicts in the work–family interface with the selected job outcomes of
frontline employees. Tourism management, 28(1), 238-252.
Kaiser HF. Little Jiffy, Mark IV. Educational and Psychological Measurement.
1974;34:111-7.
Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-
36
King, W. R., & Marks, P. V. (2008). Motivating knowledge sharing through a
knowledge management system. Omega, 36(1), 131-146.
Kelleher, D., & Levene, S. (2001). Knowledge management: a guide to good
practice. London: British Standards Institution.
Khamis, H. J., & Kepler, M. (2010). Sample size in multiple regression: 20+ 5K.
Journal of Applied Statistical Science, 17(4), 505.
Khodabakhshi.F and Abbassi.B (20152). The relationship between HRM
Practices and Social capital via the Moderating Role of Organizational
Performance in Islamic Azad universities of Gilan province, Iran. Islamic Azad
University, Astara, Iran knowledge repositories: an empirical investigation.
MIS quarterly, 113-143.
Ko, D. G., Kirsch, L. J., & King, W. R. (2005). Antecedents of knowledge transfer
from consultants to clients in enterprise system implementations. MIS
quarterly, 59-85.
LEE, Yuk Ling Angie Student Number: C3173954
170
Kogut, B., & Zander, U. (1996). What firms do? Coordination, identity, and
learning. Organization science, 7(5), 502-518.
Kontinen, T., & Ojala, A. (2010). The internationalization of family businesses:
A review of extant research. Journal of Family Business Strategy, 1(2), 97-107.
Koskinen, K. U., Pihlanto, P., & Vanharanta, H. (2003). Tacit knowledge
acquisition and sharing in a project work context. International journal of
project management, 21(4),281-290.
Kotey, B., & Folker, C. (2007). Employee training in SMEs: Effect of size and
firm type—Family and nonfamily. Journal of Small Business Management,
45(2), 214-238.
Kramer, R. M., & Brewer, M. B. 1984. Effects of Group Identity on Resource
Use in a Simulated Commons Dilemma. Journal of Personality and social
Psychology, 46(5).
Kramer, R. M., & Tyler, T. R. (1996). Trust in organisations. Frontiers of theory
and research, Sage.
Lado, A. A., & Wilson, M. C. (1994). Human resource systems and sustained
competitive advantage: A competency-based perspective. Academy of
management review, 19(4), 699-727.
Lai, S. C. E. (2010). The effect of family ownership on knowledge sharing in
electronics enterprises in southern China (Doctoral dissertation).
Lam, A., & Lambermont-Ford, J. P. (2010). Knowledge sharing in
organisational
LEE, Yuk Ling Angie Student Number: C3173954
171
contexts: a motivation-based perspective. Journal of Knowledge
Management, 14(1), 51-66.
Lansberg, I. (1999). Succeeding generations: Realizing the dream of families in
business. Harvard Business Press.
Leavitt, K., Mitchell, T. R., & Peterson, J. (2010). Theory pruning: Strategies to
reduce our dense theoretical landscape. Organizational Research Methods,
13(4), 644-667.
Lewin, K. (1940). Part One: Formalization and Progress in Psychology.
Lank, E. (1997) Leveraging invisible assets: The human factor, Journal of
Long Range Planning, 30, 3, pp. 406-412.
Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral
participation. Cambridge university press.
Leavitt, K., Mitchell, T. R., & Peterson, J. (2010). Theory pruning: Strategies to
reduce our dense theoretical landscape. Organizational Research Methods,
13(4), 644-667.
Lepak, D. P., Smith, K. G., & Taylor, M. S. (2007). Value creation and value
capture: a multilevel perspective. Academy of management review, 32(1),
180-194.
Lesser, E. L. (2000). Knowledge and social capital: Foundations and
applications.
Routledge.
LEE, Yuk Ling Angie Student Number: C3173954
172
Lee Sr, J. (2000). Knowledge management: The intellectual revolution. IIE
Solutions, 32(10), 34-34.
Lechner, W. V., Shadur, J. M., Banducci, A. N., Grant, D. M., Morse, M., &
Lejuez, C. W. (2014).The mediating role of depression in the relationship
between anxiety sensitivity and alcohol dependence. Addictive behaviors,
39(8), 1243-1248.
Leonard-Barton, D. (1995). Wellspring of knowledge. Harvard Business School
Press, Boston, MA.
Lewin, K. (1945). The research center for group dynamics at Massachusetts
Institute of Technology. Sociometry, 8(2), 126-136.
Levin, D. Z., & Cross, R. (2004). The strength of weak ties you can trust: The
mediating role of trust in effective knowledge transfer. Management science,
50(11), 1477-1490.
Locke, E. A. (1996). Motivation through conscious goal setting. Applied and
preventive psychology, 5(2), 117-124.
Lorenzen, M., & Mahnke, V. (2002). Global strategy and the acquisition of
local knowledge: how MNCs enter regional knowledge clusters. Aalborg and
Copenhagen: Danish Research Unit of Industrial Dynamics, DRUID Working
paper Series, (2002-8).
Long, R. G., & Chrisman, J. J. (2014). Management succession in family
business (pp. 371-387). L. Melin, M. Nordqvist, & P. Sharma (Eds.). Sage
Publications: London, UK.
LEE, Yuk Ling Angie Student Number: C3173954
173
Lovell, B., & Wetherell, M. A. (2016). Behaviour problems of children with ASD
and perceived stress in their caregivers: The moderating role of trait
emotional intelligence? Research in Autism Spectrum Disorders, 28, 1-6.
Licorish, S. A., & MacDonell, S. G. (2014). Understanding the attitudes,
knowledge sharing behaviors and task performance of core developers: A
longitudinal study. Information and Software Technology.
Lin, C. (2007). To share or not to share: modeling knowledge sharing using
exchange ideology as a moderator. Personnel Review, 36(3), pp. 457-475.
Lin, C. P. (2007). To share or not to share: Modeling tacit knowledge sharing,
its mediators and antecedents. Journal of business ethics, 70(4), 411-428.
Lin, H. F. (2014). Contextual factors affecting knowledge management
diffusion in SMEs. Industrial Management & Data Systems, 114(9), 1415-1437.
Ling, W. (2010). From “Made in Hong Kong” to “Designed in Hong Kong”:
Searching for an Identity in Fashion. Visual Anthropology, 24(1-2), 106-123.
Long, R. G., & Chrisman, J. J. (2014). Management succession in family
business (pp. 371-387). L. Melin, M. Nordqvist, & P. Sharma (Eds.). Sage
Publications: London, UK.
Lin, C. P. (2007). To share or not to share: Modeling tacit knowledge sharing,
its mediators and antecedents. Journal of business ethics, 70(4), 411-428.
Ling, W. (2010). From “Made in Hong Kong” to “Designed in Hong Kong”:
Searching for an Identity in Fashion. Visual Anthropology, 24(1-2), 106-123.
LEE, Yuk Ling Angie Student Number: C3173954
174
Looney, S. W., & Gulledge Jr, T. R. (1985). Use of the correlation coefficient
with normal probability plots. The American Statistician, 39(1), 75-79.
Lubans, D. R., Hesketh, K., Cliff, D. P., Barnett, L. M., Salmon, J., Dollman, J., &
Hardy, L.L. (2011). A systematic review of the validity and reliability of
sedentary behaviour measures used with children and adolescents. Obesity
reviews, 12(10), 781-799.
Lurey, J. S., & Raisinghani, M. S. (2001). An empirical study of best practices in
virtual teams. Information & Management, 38(8), 523-544.
Lynn, B. E. (2000). FEATURE EDITORIAL-INTELLECTUAL CAPITAL-There's real
wealth in intellectual capital. The thing is, you've got to uncover and manage
it. Ivey Business Journal, 64(3), 48-52.
M., Lewis, P., & Thornhill, A. (2011). Research methods for business students,
5/e. Pearson Education India.
Machin, M. A., & Treloar, C. A. (2004). Predictors of motivation to learn when
training is mandatory. In Proceedings of the 39th Australian Psychological
Society Annual Conference: Psychological Science in Action (pp. 157-161).
Australian Psychological Society.
Mackey, A., & Gass, S. M. (2015). Second language research: Methodology
and design. Routledge.
Macneil, C. (2001). The supervisor as a facilitator of informal learning in work
teams. Journal of workplace learning, 13(6), 246-253.
Madhavan, R., & Grover, R. (1998). From embedded knowledge to embodied
LEE, Yuk Ling Angie Student Number: C3173954
175
knowledge: New product development as knowledge management. The
Journal of marketing, 1-12.
Marketwired Press Release (10, Dec,2014). New Study Reveals Knowledge
Workers Mostly Do Rework, accessed 3, July , 2016.
https://uk.finance.yahoo.com/news/study-reveals-knowledge-workers-
mostly-163000478.html
Makadok, R. (2003). Doing the right thing and knowing the right thing to do:
Why the whole is greater than the sum of the parts. Strategic Management
Journal, 24(10), 1043-1055.
Martins, L. L., Eddleston, K. A., & Veiga, J. F. (2002). Moderators of the
relationship between work-family conflict and career satisfaction. Academy of
Management Journal, 45(2), 399-409.
Malhotra, N. K. (2008). Marketing Research: An Applied Orientation, 5/E.
Pearson Education India.
Mavraki, D., & Akoumianakis, D. (2014, July). Using Conversational Knowledge
Management as a lens for Virtual Collaboration in the course of small group
activities. In Information, Intelligence, Systems and Applications, IISA 2014,
The 5th International Conference on (pp. 7-12).
Mackey, A., & Gass, S. M. (2015). Second language research: Methodology
and design. Routledge.
Malhotra, N. K., Kim, S. S., & Agarwal, J. (2004). Internet users' information
privacy concerns (IUIPC): The construct, the scale, and a causal model.
Information Systems Research, 15(4), 336-355.
LEE, Yuk Ling Angie Student Number: C3173954
176
Malhotra, N. (2008) Marketing Research: An Applied Orientation, 4th ed.,
174 Pearson/Prentice-Hall, Upper Saddle River, NJ.
Malhotra, N. K., Kim, S. S., & Agarwal, J. (2004). Internet users' information
privacy concerns (IUIPC): The construct, the scale, and a causal model.
Information Systems Research, 15(4), 336-355.
Mann, C. J. (2003). Observational research methods. Research design II:
cohort, cross sectional, and case-control studies. Emergency Medicine Journal,
20(1), 54-60.
Marsick, V. J., & Watkins, K. E. (2001). Informal and incidental learning. New
directions for adult and continuing education, (89), 25-34.
McClelland, G. H., & Judd, C. M. (1993). Statistical difficulties of detecting
interactions and moderator effects. Psychological bulletin, 114(2), 376.
McDermott, R., & O'dell, C. (2001). Overcoming cultural barriers to sharing
knowledge. Journal of knowledge management, 5(1), 76-85.
Melin, L., Nordqvist, M., & Sharma, P. (Eds.). (2013). The SAGE handbook of
family business. Sage.
Menkhoff, T., Chay, Y. W., Evers, H. D., & Loh, B. (2005). Leadership in
Knowledge sharing: Creating Value through Collaboration. Journal of Asian
Business, 22(1), pp. 265-281.
LEE, Yuk Ling Angie Student Number: C3173954
177
Mitchell, H. J. (2003). Technology and Knowledge Management: Is Technology
Just an Enabler or. Knowledge management: Current issues and challenges,
66.
Miller, D., & Le Breton-Miller, I. (2005). Management insights from great and
struggling family businesses. Long Range Planning, 38(6), 517-530.
Miller, D., & Breton‐Miller, L. (2006). Family governance and firm
performance: Agency, stewardship, and capabilities. Family business review,
19(1), 73-87.
Mintzberg, H. (1994). The fall and rise of strategic planning. Harvard business
review, 72(1), 107-114.
Morck, R., & Yeung, B. (2003). Agency problems in large family business
groups. Entrepreneurship Theory and Practice, 27(4), 367-382.
Molly, V., Laveren, E., & Deloof, M. (2010). Family business succession and its
impact on financial structure and performance. Family Business Review, 23(2),
131-147.
Mooradian, N. (2005). Tacit knowledge: philosophic roots and role in KM.
Journal of knowledge management, 9(6), 104-113.147.
Mooradian, T., Renzl, B., & Matzler, K. (2006). Who trusts? Personality, trust
and knowledge sharing. Management learning, 37(4), 523-540.
Mukaka, M. M. (2012). A guide to appropriate use of Correlation coefficient in
medical research. Malawi Medical Journal, 24(3), 69-71.
LEE, Yuk Ling Angie Student Number: C3173954
178
Myers, M. D. (1997). Qualitative research in information systems.
Management Information Systems Quarterly, 21, 241-242.
Nahapiet, J., & Ghoshal, S. (1998). Social capital, intellectual capital, and the
organizational advantage. Academy of management review, 23(2), 242-266.
Nunnally, J. (1978). Psychometric methods, 2nd ed., McGraw-hill, N.Y.
Nielsen, B. B., & Nielsen, S. (2009). Learning and innovation in international
strategic alliances: An empirical test of the role of trust and tacitness. Journal
of Management Studies, 46(6), 1031-1056.
Nimon, K., Henson, R. K., & Gates, M. S. (2010). Revisiting interpretation of
canonical correlation analysis: A tutorial and demonstration of canonical
commonality analysis. Multivariate Behavioral Research, 45(4), 702-724.
Nishii, L. H., Lepak, D. P., & Schneider, B. (2008). Employee attributions of the
“why” of HR practices: Their effects on employee attitudes and behaviors, and
customer satisfaction. Personnel psychology, 61(3), 503-545.
Noe, R. A. (2010). Employee training and development. McGraw-Hill/Irwin.
Nonaka, I. (1994), “A dynamic theory of organizational knowledge creation”,
Organizational Science, Vol. 5 No. 1, pp. 14‐37
Nonaka, I., & Takeuchi, H. (1995). The Knowledge-Creating Company. How
Japanese Companies Create the Dynamics of Innovation. Oxford: Oxford
University Press.
Nonaka, I., & Konno, N. (1998). The concept of" ba": Building a foundation for
knowledge creation. California management review, 40(3), 40-54.
LEE, Yuk Ling Angie Student Number: C3173954
179
Nonaka, I., & Toyama, R. (2003). The knowledge-creating theory revisited:
knowledge creation as a synthesizing process. Knowledge management
research & practice, 1(1), 2-10.
Nonaka, I., Toyama, R., & Konno, N. (2000). SECI, Ba and leadership: a unified
model of dynamic knowledge creation. Long range planning, 33(1), 5-34.
Nonaka, I. (2008). The knowledge-creating company. Harvard Business Review
Press.
North, J. (2015). INDIVIDUAL INTRAPRENEURSHIP IN ORGANISATIONS: A NEW
MEASURE OF INTRAPRENEURIAL OUTCOMES (Doctoral dissertation, University
of York).
Norusis, M. J. M. J. (1994). SPSS: SPSS advanced statistics 6.1. SPSS Inc.,.
O’brien, R. M. (2007). A caution regarding rules of thumb for variance inflation
factors. Quality & Quantity, 41(5), 673-690.
Odusami, K. T., Iyagba, R. R. O., & Omirin, M. M. (2003). The relationship
between project leadership, team composition and construction project
performance in Nigeria. International journal of project management, 21(7),
519-527
Okhuysen, G. A., & Eisenhardt, K. M. (2002). Integrating knowledge in groups:
How formal interventions enable flexibility. Organization Science, 13(4), 370-
386.
LEE, Yuk Ling Angie Student Number: C3173954
180
Oldham, G. R. (2003). Stimulating and supporting creativity in organizations.
Managing knowledge for sustained competitive advantage, 243-273.
Orlikowski, W. J. (2002). Knowing in practice: Enacting a collective capability in
distributed organizing. Organization science, 13(3), 249-273.
.
Oudkerk Pool, I. R. (2016). To which extent does Job crafting mediate or
(negatively or positively) moderate the relationship between Human Resource
Management and Work engagement?
Pallant, J. (2001). SPSS Survival Manual: A Step by Step Guide to Data Analysis
Using SPSS for Windows (Versions 10 and 11): SPSS Student Version 11.0 for
Windows. Open University Press.
Peng, D. X., & Lai, F. (2012). Using partial least squares in operations
management research: A practical guideline and summary of past research.
Journal of Operations Management, 30(6), 467-480.
Piccolo, R. F., & Colquitt, J. A. (2006). Transformational leadership and job
behaviors: The mediating role of core job characteristics. Academy of
Management journal, 49(2), 327-340
Politis, J. D. (2003). The connection between trust and knowledge
management: what are its implications for team performance. Journal of
knowledge management, 7(5), 55-66.
Polanyi, M. (1966), The Tacit Dimension, Routledge and Kegan Paul, London
Quigley, J. V. (1994). Vision: How leaders develop it, share it, and sustain it.
Business Horizons, 37(5), 37-41.
LEE, Yuk Ling Angie Student Number: C3173954
181
Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for
assessing and comparing indirect effects in multiple mediator models.
Behavior research methods, 40(3), 879-891.
Ralston, D. A., Holt, D. H., Terpstra, R. H., & Kai-Cheng, Y. (2008). The impact
of national culture and economic ideology on managerial work values: A study
of the United States, Russia, Japan, and China. Journal of International
Business Studies, 39(1), 8-26.
Radaelli, G., Lettieri, E., & Masella, C. (2015). Physicians’ willingness to share:
a TPB-based analysis. Knowledge Management Research & Practice, 13(1), 91-
104.
Redding, S. G., & Martyn-Johns, T. A. (1979). Paradigm differences and their
relation to management, with reference to Southeast Asia. Organizational
functioning in a cross-cultural perspective. Kent, Ohio: Comparative
Administration Research Unit, Kent State University.
Redding, S.G. (1982). Results-orientation and the Orient: The role of
individualism as a cultural determinant of Western management techniques.
Asian Regional Training and Development Organization.
Redding, S. G., & Michael, N. (1983). The role of “face” in the organizational
perceptions of Chinese managers. International Studies of Management &
Organization,13(3),92-123.
Redding, S. G. (1984). Varieties of the iron rice bowl. The Hong Kong Manager,
30,1990.
LEE, Yuk Ling Angie Student Number: C3173954
182
Redding, G., & Wong, G. Y. (1986). The psychology of Chinese Organizational-
behaviour.
Redding, G. (1990). The spirit of Chinese capitalism (Vol. 22). Walter de
Gruyter.
Reid, F. (2003). Creating a knowledge-sharing culture among diverse business
units. Employment Relations Today, 30(3), 43.
Reinholt, M. I. A., Pedersen, T., & Foss, N. J. (2011). Why a central network
position isn't enough: The role of motivation and ability for knowledge sharing
in employee networks. Academy of Management Journal, 54(6), 1277-1297.
Renwick, D. W., Redman, T., & Maguire, S. (2013). Green human resource
management: A review and research agenda. International Journal of
Management Reviews, 15(1), 1-14.
Richey, R. C., Klein, J. D., & Tracey, M. W. (2010). The instructional design
knowledge base: Theory, research, and practice. Routledge.
Rietveld, T., & Van Hout, R. (1993). Statistical techniques for the study of
language and language behaviour. Walter de Gruyter.
Rogaten, J., Moneta, G. B., & Spada, M. M. (2013). Academic performance as a
function of approaches to studying and affect in studying. Journal of
Happiness Studies, 14(6), 1751-1763.
Rooke, J., & Clark, L. (2005). Learning, knowledge and authority on site: a case
study of safety practice. Building Research & Information, 33(6), 561-570.
LEE, Yuk Ling Angie Student Number: C3173954
183
Rutherford, M. W., Kuratko, D. F., & Holt, D. T. (2008). Examining the Link
Between Familiness” and Performance: Can the F‐PEC Untangle the Family
Business Theory Jungle? Entrepreneurship theory and practice, 32(6), 1089-
1109.Ryan, R. M., &
Salis, S., & Williams, A. M. (2010). Knowledge Sharing through Face‐to‐Face
Communication and Labour Productivity: Evidence from British Workplaces.
British Journal of Industrial Relations, 48(2), 436-459.
Salvato, C., & Melin, L. (2008). Creating value across generations in Family‐
Controlled businesses: The role of family social capital. Family Business
Review, 21(3), 259-276.
Sameroff, A. J., & Mackenzie, M. J. (2003). Research strategies for capturing
transactional models of development: The limits of the possible. Development
and psychopathology, 15(03), 613-640.
Samnani, A. K., & Singh, P. (2014). Performance-enhancing compensation
practices and employee productivity: The role of workplace bullying. Human
Resource Management Review, 24(1), 5-16.Saunders, M. N., Saunders,
Sarala, R. M., & Vaara, E. (2010). Cultural differences, convergence, and
crossvergence as explanations of knowledge transfer in international
acquisitions. Journal of International Business Studies, 41(8), 1365-1390.
Salis, S., & Williams, A. M. (2010). Knowledge Sharing through Face‐to‐Face
Communication and Labour Productivity: Evidence from British Workplaces.
British Journal of Industrial Relations, 48(2), 436-459.
LEE, Yuk Ling Angie Student Number: C3173954
184
Scarpello, V., & Carraher, S. M. (2008). Are pay satisfaction and pay fairness
the same construct? A cross-country examination among the self-employed in
Latvia, Germany, the UK, and the USA. Baltic Journal of Management, 3(1),
23-39.
Schmidt, T. (2010). Absorptive capacity—one size fits all? A firm‐level analysis
of absorptive capacity for different kinds of knowledge. Managerial and
Decision Economics, 31(1),1-18.
Sekaran, U., & Bougie, R. (2003 Research methods for business: A skill building
approach. John Wiley and Sons Inc., New York.
Shalley, C. E., Zhou, J., & Oldham, G. R. (2004). The effects of personal and
contextual characteristics on creativity: where should we go from here?
Journal of management, 30(6), 933-958.
Sharma, S., Durand, R. M., & Gur-Arie, O. (1981). Identification and analysis of
moderator variables. Journal of marketing research, 291-300.
Sharma, P., Chrisman, J. J., & Chua, J. H. (1997). Strategic management of the
family business: Past research and future challenges. Family business review,
10(1), 1-35
Sharma, N. (2003). The role of pure and quasi-moderators in services: an
empirical investigation of ongoing customer–service-provider relationships.
Journal of Retailing and Consumer Services, 10(4), 253-262.
Sharma, P. (2004). An overview of the field of family business studies: Current
status and directions for the future. Family business review, 17(1), 1-36.
LEE, Yuk Ling Angie Student Number: C3173954
185
Sharma, P., & Irving, P. G. (2005). Four bases of family business successor
commitment: Antecedents and consequences. Entrepreneurship Theory and
Practice, 29(1), 13-33.
Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality
(complete samples). Biometrika, 52(3/4), 591-611.
Shih, H. A., Chiang, Y. H., & Hsu, C. C. (2006). Can high performance work
systems really lead to better performance? International journal of
Manpower, 27(8), 741-763.
Shin, H., Collier, D. A., & Wilson, D. D. (2000). Supply management orientation
and supplier/buyer performance. Journal of operations management, 18(3),
317-333.
Sirmon, D. G., Arregle, J. L., Hitt, M. A., & Webb, J. W. (2008). The role of
family influence in firms' strategic responses to threat of imitation.
Entrepreneurship Theory and Practice, 32(6), 979-998.
Skinner, D., Tagg, C., & Holloway, J. (2000). Managers and Research The Pros
and Cons of Qualitative Approaches. Management Learning, 31(2), 163-179.
Srivastava, A. P., & Dhar, R. L. (2015). Training comprehensiveness: construct
development and relation with role behaviour. European Journal of Training
and Development, 39(7), 641-662.
Stephens, M. (1974). EDF Statistics for Goodness of Fit and Some
Comparisons. Journal of the American Statistical Association
LEE, Yuk Ling Angie Student Number: C3173954
186
Stewart, T. (1997). Intellectual Capital: How the Knowledge Economy Is
Creating New Challenges for Corporations & New Opportunities for the People
Who Work for Them.
Stewart, T., & Ruckdeschel, C. (1998). Intellectual capital: The new wealth of
organizations.
Stewart, T. A. (2007). The wealth of knowledge: Intellectual capital and the
twenty- first century organization. Crown Business.
Stewart, G. L., & Barrick, M. R. (2000). Team structure and performance:
Assessing the mediating role of intrateam process and the moderating role of
task type. Academy of management Journal, 43(2), 135-148.
Streiner, D. L., Norman, G. R., & Cairney, J. (2014). Health measurement
scales: a practical guide to their development and use. Oxford University
Press, USA.
Stone-Romero, E. F., & Liakhovitski, D. (2002). Strategies for detecting
moderator variables: A review of conceptual and empirical issues. Research in
personnel and human resources management, 21, 333-372.
Suhr, D. D. (2006). Exploratory or confirmatory factor analysis? (pp. 1-17).
Cary: SAS Institute.
Sveiby, K. E., & Risling, A. (1986). The know-how company. Liber, Malmo.
LEE, Yuk Ling Angie Student Number: C3173954
187
Siemsen, E., Roth, A. V., & Balasubramanian, S. (2008). How motivation,
opportunity, and ability drive knowledge sharing: The constraining- factor
model. Journal of Operations Management, 26(3), 426-445.
Silverstein, S. M. (2010). Bridging the gap between extrinsic and intrinsic
motivation in the cognitive remediation of schizophrenia. Schizophrenia
bulletin,36(5), 949-956.
Sirmon, D. G., & Hitt, M. A. (2003). Managing resources: Linking unique
resources, management, and wealth creation in family firms.
Entrepreneurship theory and practice, 27(4), 339-358.
Smith, E. A. (2000). Applying knowledge-enabling methods in the classroom
and in the workplace. Journal of workplace learning, 12(6), 236-244.
Smith, E. A. (2001). The role of tacit and explicit knowledge in the workplace.
Journal of knowledge Management, 5(4), 311-321.
Snell, S. A., & Dean, J. W. (1992). Integrated manufacturing and human
resource management: A human capital perspective. Academy of
Management journal, 35(3), 467-504.
Swap, W., Leonard, D., & Mimi Shields, L. A. (2001). Using mentoring and story
telling to transfer knowledge in the workplace. Journal of management
information systems, 18(1), 95-114.
Subramaniam, M., & Youndt, M. A. (2005). The influence of intellectual capital
on the types of innovative capabilities. Academy of Management journal,
48(3), 50-463.
LEE, Yuk Ling Angie Student Number: C3173954
188
Sull, D. N., & Wang, Y. (2013). Made in China: What western managers can
learn from trailblazing Chinese entrepreneurs. Harvard Business Press.
Szulanski, G. (1996). Exploring internal stickiness: Impediments to the transfer
of best practice within the firm. Strategic management journal,17(S2), 27-43.
Tabachnick, B. G., Fidell, L. S., &Osterlind, S. J. (2001). Using multivariate
statistics.
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics, 5th.
Needham Height, MA: Allyn & Bacon.
Thompson, E. R. (2001). Business integration across the Hong Kong-Chinese
border: Patterns and explanations in the garment industry. In Asia-Pacific
transitions (pp. 224-238). Palgrave Macmillan UK.
Tsoukas, H. (1996). The firm as a distributed knowledge system: a
constructionist approach. Strategic management journal, 17(S2), 11-25.
Tzafrir, S. S. (2005). The relationship between trust, HRM practices and firm
performance. The International Journal of Human Resource Management,
16(9), 1600-1622.
Uhlaner, L. M., van Goor-Balk, H. J. M., & Masurel, E. (2004). Family business
and corporate social responsibility in a sample of Dutch firms. Journal of Small
Business and Enterprise Development, 11(2), 186-194.
Van den Hooff, B., & De Ridder, J. A. (2004). Knowledge sharing in context: the
influence of organizational commitment, communication climate and CMC
use on knowledge sharing. Journal of knowledge management, 8(6), 117-130.
LEE, Yuk Ling Angie Student Number: C3173954
189
Van den Hooff, B., & Hendrix, L. (2004). Eagerness and willingness to share:
The relevance of different attitudes towards knowledge sharing. Paper
presented at the Fifth European Conference on Organizational Knowledge,
Learning and Capabilities, Innsbruck, Austria.
Van Lange, P. A., Liebrand, W. B., Messick, D. M., & Wilke, H. A. (1992).
Introduction and literature review. Social dilemmas: Theoretical issues and
research findings, 3, 28.
Van Wijk, R., Jansen, J. J., & Lyles, M. A. (2008). Inter‐and intra‐
organizational knowledge transfer: a meta‐analytic review and assessment
of its antecedents and consequences. Journal of Management Studies, 45(4),
830-853.
Vodde, R. F. (2012). Changing paradigms in police training: Transitioning from
a traditional to an andragogical model. In Police organization and training
(pp. 27-44). Springer New York.
VonKortzfleisch, H. F., & Mergel, I. (2002). Getting Over “Knowledge is
Power”: Incentive Systems for Knowledge Management in Business
Consulting
VonKortzfleisch, H. F., & Mergel, I. (2002). Getting Over “Knowledge is
Power”: Incentive Systems for Knowledge Management in Business Consulting
Companies. Knowledge Mapping and Management, 244-253.
LEE, Yuk Ling Angie Student Number: C3173954
190
Usoro, A., Sharratt, M. W., Tsui, E., & Shekhar, S. (2007). Trust as an
antecedent to knowledge sharing in virtual communities of practice.
Knowledge Management Research & Practice, 5(3), 199-212.
Wah, L. (2000). Making knowledge stick. The knowledge management
yearbook, 2001, 145-156.
Waldman, D. A., & Spangler, W. D. (1989). Putting together the pieces: A
closer look at the determinants of job performance. Human Performance,
2(1), 29-59.
Wan Hooi, L., & Sing Ngui, K. (2014). Enhancing organizational performance of
Malaysian SMEs: The role of HRM and organizational learning capability.
International Journal of Manpower, 35(7), 973-995.
Wang, Y., & Ahmed, P. K. (2009). The moderating effect of the business
strategic orientation on eCommerce adoption: Evidence from UK family run
SMEs. The Journal of Strategic Information Systems, 18(1), 16-30.
Wang, S., Noe, R. A., & Wang, Z. M. (2014). Motivating knowledge sharing in
knowledge management systems a quasi–field experiment. Journal of
Management, 40(4), 978-1009.
Wan Hooi, L., & Sing Ngui, K. (2014). Enhancing organizational performance of
Malaysian SMEs: The role of HRM and organizational learning capability.
International Journal of Manpower, 35(7), 973-995.
Wasko, M. M., & Faraj, S. (2000). “It is what one does”: why people
participate and help others in electronic communities of practice. The Journal
of Strategic Information Systems, 9(2), 155-173.
LEE, Yuk Ling Angie Student Number: C3173954
191
Wenger, E., McDermott, R. A., & Snyder, W. (2002). Cultivating communities
of practice: A guide to managing knowledge. Harvard Business Press.
Whittaker, S. and Marchington, M. (2003). ‘Devolving HR responsibility to the
line: threat, opportunity or partnership?’. Employee Relations, 25: 3, 245–261.
Williamson, O. E. (1999). Strategy research: governance and competence
perspectives, Strategic Management Journal,20,1087–1108
Williams, B., Onsman, A., & Brown, T. (2010). Exploratory factor analysis: A
five-step guide for novices. Australasian Journal of Paramedicine, 8(3).
Winter, G. (2000). A comparative discussion of the notion of 'validity' in
qualitative and quantitative research. The qualitative report, 4(3), 1-14.
Winer, B. J., Brown, D. R., & Michels, K. M. (1971). Statistical principles in
experimental design (Vol. 2, p. 596). New York: McGraw-Hill.
Wolters, K. (2014). Narrative review on how mediating mechanisms influence
the relationship between employees’ perception of HR practices and employee
performance.
Wood, S. J., & Wall, T. D. (2007). Work enrichment and employee voice in
human resource management-performance studies. The International Journal
of Human Resource Management, 18(7), 1335-1372.
Woolcock, M. (2001). The place of social capital in understanding social and
economic outcomes. Canadian journal of policy research, 2(1), 11-17.
LEE, Yuk Ling Angie Student Number: C3173954
192
Wong, K. Y., & Aspinwall, E. (2005). “An empirical study of important factors
for knowledge-management adoption in the SME Sector”. Journal of
Knowledge Management, 9(3), pp. 64-82.
Wu, Y., Balasubramanian, S., & Mahajan, V. (2004). When is a preannounced
new? product likely to be delayed? Journal of Marketing, 68(2), 101-113.
Wu, X., Chen, Q., Zhou, W., & Guo, J. (2010). A review of Mobile Commerce
consumers' behaviour research: consumer acceptance, loyalty and
continuance (2000-2009). International Journal of Mobile Communications,
8(5), 528-560.
Yang, J. (2007). “Knowledge sharing: Investigating appropriate leadership
roles and collaborative culture”. Tourism Management, 28(2), pp. 530-543.
Yew Wong, K., & Aspinwall, E. (2005). An empirical study of the important
factors for knowledge-management adoption in the SME sector. Journal of
knowledge management, 9(3), 64-82.
Yu, B., Singh, M. P., & Sycara, K. (2004, August). Developing trust in large-scale
peer-to-peer systems. In Multi-Agent Security and Survivability, 2004 IEEE
First Symposium on (pp. 1-10). IEEE.Companies. Knowledge Mapping and
Management, 244-253.
Yuan, D., Hua, Z., & Junxi, Z. (2008). The financial and operating performance
of Chinese family owned listed firms. Management International
Review,48(3), 297-318.
Zaheer, S., & Zaheer, A. (2006). Trust across borders. Journal of International
Business Studies, 37(1), 21-29
LEE, Yuk Ling Angie Student Number: C3173954
193
Zahra, S. A. (2003). International expansion of US manufacturing family
businesses: The effect of ownership and involvement. Journal of business
venturing, 18(4), 495-512.
Zahra, S. A., Hayton, J. C., & Salvato, C. (2004). Entrepreneurship in family vs.
Non‐Family firms: A Resource‐Based analysis of the effect of organizational
culture. Entrepreneurship theory and Practice, 28(4), 363-381.
Zahra, S. A. (2005). Entrepreneurial risk taking in family firms. Family Business
Review, 18(1), 23-40.
Zahra, S. A., Sapienza, H. J., & Davidsson, P. (2006). Entrepreneurship and
dynamic capabilities: a review, model and research agenda. Journal of
Management studies, 43(4), 917-955.
Zahra, S. A., Neubaum, D. O., & Larrañeta, B. (2007). Knowledge sharing and
technological capabilities: The moderating role of family involvement. Journal
of Business research, 60(10), 1070-1079.
Zhang, P. C., Zhang, L. B., Hou, Z. R., & Zhang, K. J. (2008). “The impact of
values and leadership on knowledge integration behaviors in medical projects
teams”. 2008 International Conference on Computer Science and Software
Engineering, 5, pp.242-245.
Zahra, S. A. (2012). Organizational learning and entrepreneurship in family
firms: Exploring the moderating effect of ownership and cohesion. Small
business economics, 38(1), 51-65.
LEE, Yuk Ling Angie Student Number: C3173954
194
Zarraga, C., & Bonache, J. (2003). Assessing the team environment for
knowledge sharing: an empirical analysis.International Journal of Human
Resource Management,14(7),1227-1245.
Zellweger, T. M., Eddleston, K. A., & Kellermanns, F. W. (2010). Exploring the
concept of familiness: Introducing family firm identity. Journal of family
business strategy, 1(1), 54-63.
Zikmund, W.G. (2003) Business Research Methods. 7th Edition, Thomson
South Western, Ohio
LEE, Yuk Ling Angie Student Number: C3173954
195
Appendix A
Email Invitation
Dr. Ashish Malik Faculty of Business and Law Newcastle Business School Tel: (02) 43484133 Email: [email protected]
Subject: Information Statement for the Research Project: The impact of AMO model on knowledge sharing (KS) in Family controlled businesses (FCBs) in
Hong Kong’s Clothing industry (HKCI). Dear Sir/Madam,
This study is a research project for the Doctor of Business Administration (DBA) degree at The University of Newcastle, Australia. It is being carried out by Ms. Lee Yuk Ling Angie (Email: [email protected]), under the supervision of Dr. Ashish Malik (Email: [email protected]). Faculty of Business and Law at the University of Newcastle. The research will be undertaken in Hong Kong.
You are invited to participate in this anonymous study employing an online
questionnaire-based design focusing on the impact of AMO (ability, motivation and
opportunity) model on knowledge sharing (KS) in Family controlled businesses
(FCBs) in Hong Kong clothing industry (HKCI).
I attach to this email details of the research project in the attached Organisation
Consent form(OCF), Information statement for organisation(ISO), Participant
LEE, Yuk Ling Angie Student Number: C3173954
196
Information Statement (PIS), which also contains a link to the web-based
questionnaire. I would suggest you to either save this OCF, ISO,PIS or print it for
your future records.
Please sign the OCF and have a scanned copy returned to the named researcher for
your approval. I would greatly appreciate if you could circulate this email with the
attachment to the following employees who are 18 years or over:
- The CEO
- General Manager
- Manager (or designate)
Although it is stated in the PIS, I would reiterate that under no circumstances
would any of the participants be identified in the study’s reporting.
For the details, please open and read the PIS document and click on the research eSurvey Creation link to start the survey since it will only take 15 minutes to complete it. Your participation in completing the survey is highly appreciated.
Yours sincerely
Supervisor: Dr. Ashish Malik
Student Researcher: Ms. Angie Lee
LEE, Yuk Ling Angie Student Number: C3173954
197
Appendix B
Information Statement for Organization
INFORMATION STATEMENT FOR ORGANISATION Date: 3rd, February, 2016 Dr Ashish Malik BO 1.16 Business Office, Central Coast Campus, Ourimbah, 2258 Newcastle Business School, University of Newcastle, Australia. Ph: 02-434 84133 (Extension: 84133). To The CEO/ Vice-President (or designate e.g. General manager) Organisation Name Address Hong Kong Dear Sir/Madam
Information Statement for the Research Project: The impact of AMO model on knowledge sharing (KS) in Family controlled businesses
(FCBs) in Hong Kong’s Clothing industry (HKCI).
Your organisation is invited to participate in the above mentioned research project which is being conducted by a student, Ms. Lee Yuk Ling Angie, who is undertaking the Doctor of Business Administration degree, under the supervision of Dr. Ashish Malik from the Schools of Business and Law at the University of Newcastle. The research will be undertaken in Hong Kong. Why is the research being done?
The purpose of the research is to investigate the relative importance ability, motivation and opportunity (AMO) in sharing knowledge in Family Controlled business in the Hong Kong (HK) clothing industry. More specifically, analysing the role of incentive systems, training for workers and trust and knowledge sharing in the HK clothing industry.
LEE, Yuk Ling Angie Student Number: C3173954
198
Who can participate in the research?
Your organisation is invited to participate in this study. By forwarding this email and attached link to the study’s questionnaire in the Participant Information Sheet document attached to this email message to a relevant practitioner who is in the position of a Manager/Top executive/business ownership or owner of a family owned business in the Hong Kong clothing industry your organisation provides consent to participate in this study. What choice does your organisation have? Participation in this research is entirely your organisation’s choice. By accepting our request for the distribution of the recruitment email containing the participant information statement, containing the anonymous questionnaire link, to your employees (as specified above), your organisation provides informed consent. Please note that due to the anonymous nature of the questionnaire, once you forward the survey recruitment email to participate, your organisation will not be able to withdraw from the study. How much time will it take? The questionnaire will take between 10-15 minutes. What are the risks and benefits of participating? There are no anticipated risks associated with participating in this research project. Whilst there are no anticipated benefits to you personally in participating this research project, the research aims to benefit family businesses in HK’s clothing industry by knowledge sharing and success of family business in this industry. This study also aims to provide an understanding and learning about how family factors influence knowledge sharing in the HK clothing industry. How will your privacy be protected?
As this study will use an online questionnaire, any personal details of your organisation will not be identifiable. Confidentiality of your organisation will be maintained at all times. The data collected will be used to complete statistical analysis. The collected data will be stored on a password protected computer accessible only by the student researcher and, where necessary, by the study’s Chief Investigator. Access to the data via the online questionnaire software will be through a protected password in the Student Researcher’s, and if accessed by the Chief Investigator, it will also be password protected in the Chief Investigator’s computer. For further data analysis, data inputted in the SPSS V22 software will also be password protected for additional security measures. The data will be disposed in accordance with the policy and procedures for disposing confidential materials as per the University of Newcastle’s policies. In addition, data will be retained for a minimum of 5 years as per University of Newcastle requirements. How will the information collected be used?
The collected data will be used as part of Ms Lee Yuk Ling Angie’s thesis which will be submitted to the University of Newcastle’s library. The results of this research
LEE, Yuk Ling Angie Student Number: C3173954
199
project may also be presented in academic publications such as journal articles, books and conferences. Organisations are able to receive a copy of the summary report by emailing [email protected] after October 2016. What do you need to do to participate? Please read this Organisation Information Statement and be sure you understand its contents before you provide your consent to participate. Further information If you would like further information please contact me at [email protected] Thank you for considering this invitation. Yours sincerely Ashish Malik Dr Ashish Malik Lecturer-HRM Newcastle Business School University of Newcastle Ms Lee Yuk Ling Angie Student Researcher University of Newcastle Tel: +852 93637204 Email: [email protected]. Complaints about this research This project has been approved by the University’s Human Research Ethics Committee, Approval No. H-2015-0383Should you have concerns about your rights as a participant in this research, or you have a complaint about the manner in which the research is conducted, it may be given to the researcher, or, if an independent person is preferred, to the Human Research Ethics Officer, Research Office, The Chancellery, The University of Newcastle, University Drive, Callaghan NSW 2308, Australia, telephone (02) 49216333, email Human-
[email protected]. Alternatively, you can contact Ms Yan Chau, our local Hong Kong Management Association Administrator at [email protected] or, 16/F, Tower B, Southmark , 11 Yip Hing Street, Wong Chuk Hang, HONG KONG , Phone: (852) 2774 8547
LEE, Yuk Ling Angie Student Number: C3173954
200
Appendix C
Information Statement for Organization
Date: 3rd, February, 2016 Dr. Ashish Malik Faculty of Business and Law Newcastle Business School Tel: (02) 43484133 Email: [email protected]
Information Statement for the Research Project: The impact of AMO model on knowledge sharing (KS) in Family controlled
businesses (FCBs) in Hong Kong’s Clothing industry (HKCI).
You are invited to participate in the above mentioned research project which is being conducted by a student, Ms. Lee Yuk Ling Angie, who is undertaking the Doctor of Business Administration degree, under the supervision of Dr. Ashish Malik from the Schools of Business and Law at the University of Newcastle. The research will be undertaken in Hong Kong. Why is the research being done?
The purpose of the research is to investigate the relative importance ability, motivation and opportunity (AMO) in sharing knowledge in Family Controlled business in the Hong Kong (HK) clothing industry. More specifically, analysing the role of incentive systems, training for workers and trust and knowledge sharing in the HK clothing industry. Who can participate in the research?
You are invited to participate in this questionnaire if you are aged over 18 and a relevant practitioner in the position of a Manager/Top executive/business ownership or owner of a family owned business in the Hong Kong clothing industry.
LEE, Yuk Ling Angie Student Number: C3173954
201
What would you be asked to do?
If you agree to participate after reading this participation information sheet, you will be invited to complete in an online questionnaire on knowledge sharing in the HK clothing industry by clicking on the web link provided at the end of this participant information sheet. What choice do you have?
Participation in this research is entirely your choice. Only those people who give their informed consent will be included in the project. Whether or not you decide to participate, your decision will not disadvantage you. If you do decide to participate, you may withdraw from the project at any time prior to submitting your completed questionnaire. Please note that due to the anonymous nature of the questionnaire, you will not be able to withdraw your response after it has been submitted. Please be informed that by completing the questionnaire you and your organisation will not be identifiable as the online questionnaire is anonymous.
How much time will it take?
The questionnaire should take about 10-15 minutes to complete all the sections.
What are the risks and benefits of participating?
There are no anticipated risks associated with participating in this research project. Whilst there are no anticipated benefits to you personally in participating this research project, the research aims to benefit family businesses in HK’s clothing industry by knowledge sharing and success of family business in this industry. This study also aims to provide an understanding and learning about how family factors influence knowledge sharing in the HK clothing industry. How will your privacy be protected?
As this study will use an online questionnaire, any personal details of the participants will not be disclosed, and nobody will be identifiable. Confidentiality of all respondents will be maintained at all times. The data collected will be used to complete statistical analysis. The collected data will be stored on a password protected computer accessible only by the student researcher and, where necessary, by the study’s Chief Investigator. Access to the data via the online questionnaire software will be through a protected password in the Student Researcher’s, and if accessed by the Chief Investigator, it will also be password protected in the Chief Investigator’s computer. For further data analysis, data inputted in the SPSS V22 software will also be password protected for additional security measures. The data will be disposed in accordance with the policy and procedures for disposing confidential materials as per the University of
LEE, Yuk Ling Angie Student Number: C3173954
202
Newcastle’s policies. In addition, data will be retained for a minimum of 5 years as per University of Newcastle requirements. How will the information collected be used?
The collected data will be used as part of Ms Lee Yuk Ling Angie‘s thesis which will be submitted to the University of Newcastle’s library. The results of this research project may also be presented in academic publications such as journal articles, books and conferences. Participants can request a summary of the results of this research project by sending a request to the student research by email address: [email protected].
The Participants are able to receive a copy of the summary report by emailing [email protected] after October 2016. What do you need to do to participate?
Please read this Participant Information Statement and print a copy of the same for your records so you are sure you understand its contents before you agree to participate by clicking on the link to the online questionnaire below. If there is anything you do not understand, or you have questions, please contact the student researcher or the chief investigator. After you have read and understood the participant information statement and would like to participate, please click on the link to the questionnaire. Please be informed that completion and submission of the questionnaire implies that you agree to participate. Please click on the link below if you wish to participate in the questionnaire: https://www.esurveycreator.com/s/742c3cd Further information
If you would like further information please contact Dr. Ashish Malik at the email address above to obtain further information about the project.
Thank you for considering to participate in this study. Dr Ashish Malik, Chief Investigator University of Newcastle Tel: (02) 43484133 Email: [email protected] Ms Lee Yuk Ling Angie Student Researcher University of Newcastle Tel: +852 93637204 Email: [email protected].
LEE, Yuk Ling Angie Student Number: C3173954
203
Complaints about this research This project has been approved by the University’s Human Research Ethics Committee, Approval No. H-2015-0383Should you have concerns about your rights as a participant in this research, or you have a complaint about the manner in which the research is conducted, it may be given to the researcher, or, if an independent person is preferred, to the Human Research Ethics Officer, Research Office, The Chancellery, The University of Newcastle, University Drive, Callaghan NSW 2308, Australia, telephone (02) 49216333, email Human-
[email protected]. Alternatively, you can contact Ms Yan Chau, our local Hong Kong Management Association Administrator at [email protected] or, 16/F, Tower B, Southmark , 11 Yip Hing Street, Wong Chuk Hang, HONG KONG , Phone: (852) 2774 8547
LEE, Yuk Ling Angie Student Number: C3173954
204
Appendix D
Survey
LEE, Yuk Ling Angie Student Number: C3173954
205
LEE, Yuk Ling Angie Student Number: C3173954
206
LEE, Yuk Ling Angie Student Number: C3173954
207
LEE, Yuk Ling Angie Student Number: C3173954
208
LEE, Yuk Ling Angie Student Number: C3173954
209
LEE, Yuk Ling Angie Student Number: C3173954
210
Appendix E
Frequency Tables
Appendix E1: Frequency Table of Items for “Training for workers”
Frequency Percent N Mean Stn. Deviation Item 1
TWtt
TW1 Strongly Disagree 5 4.2 Disagree 8 6.7 Moderately Disagree 19 15.1 Neither agree nor disagree 25 21.0 Moderately Agree 28 23.5 Agree 25 21.0 Strongly Agree 10 8.4 All respondents 119 4.50 1.55 Item 2 TW2 Strongly Disagree 2 1.9 Disagree 7 5.9 Moderately disagree 23 19.3 Neither agree nor disagree 19 16.0 Moderately Agree 30 25.2 Agree 31 26.1 Strongly Agree 7 5.9 All respondents 119 4.59 1.44 Item 3 TW3 Strongly Disagree 4 3.4 Disagree 15 12.6 Moderately disagree 20 16.8 Neither agree nor disagree 24 20.2 Moderately Agree 25 21.0 Agree 25 21.0 Strongly Agree 6 5.0 All respondents 119 4.26 1.56 Item 4 TW4 Strongly Disagree 5 4.2 Disagree 8 6.7 Moderately disagree 20 16.8 Neither agree nor disagree 24 20.2 Moderately Agree 32 26.9 Agree 25 21.0 Strongly Agree 5 4.2 All respondents 119 4.39 1.47 Item 5 TW5 Strongly Disagree 7 5.9 Disagree 10 8.4 Moderately disagree 22 18.5 Neither agree nor disagree 21 17.6 Moderately Agree 24 20.2 Agree 27 22.7 Strongly Agree 8 6.7
All respondents 119 4.33 1.64
Overall Perceived Value Score All respondents 4.41 1.38
LEE, Yuk Ling Angie Student Number: C3173954
211
Appendix E.2: Frequency Table of Items for “Incentive systems” Frequency Percent N Mean Stn. Deviation Item 1
TWtt
IS1 Strongly Disagree 4 3.4 Disagree 8 6.7 Moderately Disagree 20 16.8 Neither agree nor
disagree
18 15.1 Moderately Agree 37 31.1 Agree 26 21.8 Strongly Agree 6 5.0
All respondents 119 4.50 1.47 Item 2 IS2 Strongly Disagree 2 1.7 Disagree 7 5.9 Moderately Disagree 20 16.8 Neither agree nor
disagree
17 14.3 Moderately Agree 36 30.3 Agree 27 22.7 Strongly Agree 10 8.4 All respondents 119 4.67 1.44 Item 3 IS3 Strongly Disagree 3 2.5 Disagree 11 9.2 Moderately Disagree 18 15.1 Neither agree nor
disagree
17 14.3 Moderately Agree 41 34.5 Agree 22 18.5 Strongly Agree 7 5.9
All respondents 119 4.48 1.47 Item 4 IS4 Strongly Disagree 3 2.5 Disagree 7 5.9 Moderately Disagree 20 16.8 Neither agree nor
disagree
19 16.0 Moderately Agree 32 26.9 Agree 28 23.5 Strongly Agree 10 8.4
All respondents 119 4.63 1.49 Item 5 IS5 Strongly Disagree 1 .8 Disagree 11 9.2 Moderately Disagree 15 12.6 Neither agree nor
disagree
28 23.5 Moderately Agree 31 26.1 Agree 27 22.7 Strongly Agree 6 5.0 All respondents 119 4.53 1.39
Overall Perceived Value Score All respondents 4.56 1.31
LEE, Yuk Ling Angie Student Number: C3173954
212
Appendix E.3: Frequency Table of Items for “Trust”
LEE, Yuk Ling Angie Student Number: C3173954
213
Appendix E.4: Frequency Table of Items for “Knowledge sharing”
Frequen
cy
Perce
nt
N Me
an
Stn.
Deviation Item
1
TWtt
K1 Strongly
Disagree
1 .8
FK Disagree 4 3.4
Moderately
Disagree
27 22.7
Neither agree
nor disagree
23 19.3
Moderately
Agree
42 35.3
Agree 15 12.6
Strongly Agree 7 5.9
All respondents 119 4.46 1.27
Item
2
K2 Strongly
Disagree
1 .8
FK Disagree 7 5.9
Moderately
Disagree
22 18.5
Neither agree
nor disagree
25 21.0
Moderately
Agree
44 37.0
Agree 15 12.6
Strongly Agree 5 4.2
All respondents 119 4.42 1.26
Item
3
K3 Strongly
Disagree
2 1.7
FK Disagree 9 7.6
Moderately
Disagree
17 14.3
Neither agree
nor disagree
39 32.8
Moderately
Agree
28 23.5
Agree 18 15.1
Strongly Agree 6 5.0
All respondents 119 4.35 1.34
Item
4
K4 Strongly
Disagree
1 .8
FK Disagree 4 3.4
Moderately
Disagree
25 21.0
Neither agree
nor disagree
27 22.7
Moderately
Agree
32 26.9
Agree 24 20.2
Strongly Agree 6 5.0
All respondents 119 4.52 1.30
Item
5
K5 Strongly
Disagree
1 .8
FK Disagree 8 6.7
Moderately
Disagree
29 24.4
Neither agree
nor disagree
26 21.8
Moderately
Agree
25 21.0
Agree 25 21.0
Strongly Agree 5 4.2
All respondents 119 4.35 1.38
Item
6
K6 Strongly
Disagree
1 .8
IK Disagree 3 2.5
Moderately
Disagree
23 19.3
Neither agree
nor disagree
29 24.4
Moderately
Agree
35 29.4
Agree 22 18.5
Strongly Agree 6.8 5.0
All respondents 2.5 119 4.55 1.25
Item
7
K7 Disagree 1 .8
IK Moderately
Disagree
7 5.9
Neither agree
nor disagree
23 19.3
Moderately
Agree
29 24.4
Agree 31 26.1
Strongly Agree 25 21.0
Disagree 3 2.5
All respondents 119 4.42 1.29
LEE, Yuk Ling Angie Student Number: C3173954
214
Item
8
K8 Disagree 1 .8
IK Moderately
Disagree
6 5.0
Neither agree
nor disagree
21 17.6
Moderately
Agree
24 20.2
Agree 36 30.3
Strongly Agree 25 21.0
Disagree 6 5.0
All respondents 119 4.57 1.32
Item
9
K9 Disagree 1 .8
IK Moderately
Disagree
3 2.5
Neither agree
nor disagree
26 21.8
Moderately
Agree
23 19.3
Agree 35 29.4
Strongly Agree 20 16.8
Disagree 11 9.2
All respondents 119 4.61 1.35
Item
10
K10 Disagree 1 .8
IK Moderately
Disagree
4 3.4
Neither agree
nor disagree
22 18.5
Moderately
Agree
27 22.7
Agree 37 31.1
Strongly Agree 20 16.8
Disagree 8 6.7
All respondents 119 4.57 1.29
Overall Perceived Value Score All respondents 4.48 1.09
